Bayesian inference using WBDev: A tutorial for social scientists

Bayesian inference using WBDev: A tutorial for social scientists
Behavior Research Methods
2010, 42 (3), 884-897
doi:10.3758/BRM.42.3.884
Bayesian inference using WBDev:
A tutorial for social scientists
Ruud Wetzels
University of Amsterdam, Amsterdam, The Netherlands
Michael D. Lee
University of California, Irvine, California
and
Eric-Jan Wagenmakers
University of Amsterdam, Amsterdam, The Netherlands
Over the last decade, the popularity of Bayesian data analysis in the empirical sciences has greatly increased.
This is partly due to the availability of WinBUGS, a free and flexible statistical software package that comes
with an array of predefined functions and distributions, allowing users to build complex models with ease.
For many applications in the psychological sciences, however, it is highly desirable to be able to define one’s
own distributions and functions. This functionality is available through the WinBUGS Development Interface
(WBDev). This tutorial illustrates the use of WBDev by means of concrete examples, featuring the expectancyvalence model for risky behavior in decision making, and the shifted Wald distribution of response times in
speeded choice.
Psychologists who seek quantitative models for their
data face formidable challenges. Not only are data often
noisy and scarce, but they may also have a hierarchical
structure, they may be partly missing, they may have been
obtained under an ill-defined sampling plan, and they may
be contaminated by a process that is not of interest. In
addition, the models under consideration may have multiple restrictions on the parameter space, especially when
there is useful prior information about the subject matter
at hand.
In order to address these kinds of real-world challenges,
the psychological sciences have started to use Bayesian
models for the analysis of their data (e.g., Hoijtink, Klugkist, & Boelen, 2008; Lee, 2008; Rouder & Lu, 2005).
In Bayesian models, existing knowledge is quantified
by prior probability distributions and is updated upon
consideration of new data to yield posterior probability
distributions. Modern approaches to Bayesian inference
include Markov chain Monte Carlo (MCMC) sampling
(e.g., Gamerman & Lopes, 2006; Gilks, Richardson, &
Spiegelhalter, 1996), a procedure that makes it easy for
researchers to construct probabilistic models that respect
the complexities in the data, allowing almost any probabilistic model to be evaluated against data.
One of the most influential software packages for
MCMC-based Bayesian inference is known as WinBUGS
(BUGS stands for Bayesian inference using Gibbs sampling; Cowles, 2004; Lunn, Spiegelhalter, Thomas, &
Best, 2009; Lunn, Thomas, Best, & Spiegelhalter, 2000;
Sheu & O’Curry, 1998). WinBUGS comes equipped with
an array of predefined functions (e.g., sqrt for square
root and sin for sine) and distributions (e.g., the binomial
and the normal) that allow users to combine these elementary building blocks into complex probabilistic models.
For some psychological modeling applications, however, it is highly desirable to define one’s own functions
and distributions. In particular, user-defined functions
and distributions greatly facilitate the use of psychological process models such as ALCOVE (Kruschke, 1992),
the expectancy-valence (EV) model for decision making
(Busemeyer & Stout, 2002), the SIMPLE model of memory (Brown, Neath, & Chater, 2007), or the Ratcliff diffusion model of response times (RTs; Ratcliff, 1978).
The ability to implement these user-defined functions
and distributions can be achieved through the use of the
WinBUGS Development Interface (WBDev; Lunn, 2003),
an add-on program that allows the user to hand-code
functions and distributions in the programming language
Component Pascal.1 To that end, we need BlackBox, a development environment for programs such as WinBUGS,
which are written in Component Pascal.
The use of WBDev brings several advantages. For instance, complicated WBDev components lead to faster
computation than do their counterparts programmed in
straight WinBUGS code. Moreover, some models will
work properly only when implemented in WBDev. An-
R. Wetzels, [email protected]
© 2010 The Psychonomic Society, Inc.
884
PS
Bayesian Inference Using WBDev 885
other advantage of WBDev is that it compartmentalizes
the code, resulting in scripts that are easier to understand,
communicate, adjust, and debug. A final advantage of
WBDev is that it allows the user to program functions and
distributions that are simply not available in WinBUGS
but may be central components of psychological models
(Donkin, Averell, Brown, & Heathcote, 2009; Vandekerck­
hove, Tuerlinckx, & Lee, 2009).
This tutorial aims to stimulate psychologists to use
WBDev by providing four thoroughly documented examples; for both functions and distributions, we provide a
simple and a more complex example. All the examples are
relevant to psychological research.2
Our tutorial is geared toward researchers who have experience with computer programming and WinBUGS. A
gentle introduction to the WinBUGS program is provided
by Ntzoufras (2009) and Lee and Wagenmakers (2009).
Despite these prerequisites, we have tried to keep our tutorial accessible for social scientists in general.
We start our tutorial by discussing the WBDev implementation of a simple function that involves the addition
of variables. We then turn to the implementation of a
complicated function that involves the EV model (Busemeyer & Stout, 2002; Wetzels, Vandekerckhove, Tuerlinckx, & Wagenmakers, 2010). Next, we discuss the
WBDev implementation of a simple distribution, first
focusing on the binomial distribution, and then turning
to the implementation of a more complicated distribution that involves the shifted Wald distribution (Heathcote, 2004; Schwarz, 2001). For all of these examples,
we explain the crucial parts of the WBDev scripts and
the WinBUGS code. The thoroughly commented code is
available online at www.ruudwetzels.com. For each example, our explanation of the WBDev code is followed
by application to data and the graphical analysis of the
output.
Installing WBDev (BlackBox)
Before we can begin hard-coding our own functions
and distributions, we need to download and install three
programs: WinBUGS, WBDev, and BlackBox.3 To make
sure all programs function properly, they have to be installed in the order given below.
1. Install WinBUGS
WinBUGS is the core program that we will use. Download the latest version from www.mrc-bsu.cam.ac.uk/
bugs/winbugs/contents.shtml (WinBUGS14.exe). Install
the program in the default directory ./Program Files/
WinBUGS14.4 Make sure to register the software by obtaining the registration key and following the instructions;
WinBUGS will not work without it.
2. Install WinBUGS Development
Interface (WBDev)
Download WBDev from www.winbugsdevelopment
.org.uk/ (WBDev.exe). Unzip the executable in the WinBUGS directory ./Program Files/WinBUGS14. Then
start WinBUGS, open the“wbdev01_09_04.txt” file, and
follow the instructions at the top of the file. During the process, WBDev will create its own directory, /WinBUGS14/
WBDev.
3. Install BlackBox Component Builder
BlackBox can be downloaded from www.oberon.ch/
blackbox.html. At the time of writing, the latest version is
1.5. Install BlackBox in the default directory: ./­
Program
Files/BlackBox Component Builder 1.5 . Go
to the WinBUGS directory and select all files (press
“­Ctrl1A”) and copy them (press “Ctrl1C”). Next, open
the ­BlackBox directory and paste the copied files in there
(press “Ctrl1V”). Select “Yes to all” if asked about
replacing files. Once this is done, you will be able to open
BlackBox and run WinBUGS from inside BlackBox. This
completes installation of the software, and you can start to
write your own functions and distributions.
Functions
The mathematical concept of a function expresses a dependence between variables. The basic idea is that some
variables are given (the input) and, with them, other variables are calculated (the output). Sometimes, complex
models require many arithmetic operations to be performed
on the data. Because such calculations can become computationally demanding using straight WinBUGS code,
it can be convenient to use WBDev and implement these
calculations as a function. The first part of this section
will explain a problem without using WBDev. We then
will show how to use WBDev to program a simple and a
more complex function.
Example 1: A Rate Problem
A binary process has two possible outcomes. It might
be that something happens or does not happen, or either
succeeds or fails, or takes one value rather than the other.
An inference that often is important for these sorts of processes concerns the underlying rate at which the process
takes one value rather than the other. Inferences about the
rate can be made by observing how many times the process takes each value over a number of trials.
Suppose that someone plays a simple card game and
can either win or lose. We are interested in the probability
that the player wins a game. To study this problem, we
formalize it by assuming that the player plays n games
and wins k of them. These are known, or observed, data.
The unknown variable of interest is the probability q that
the player wins any one specific game. Assuming that
the games are statistically independent (i.e., that what
happened on one game does not influence the others, so
that the probability of winning is the same for all of the
games), the number of wins k follows a binomial distribution, which is written as
k ~ Binomial(q,n),
(1)
and can be read “the success count k out of a total of n trials is binomially distributed with success rate q.” In this
example, we will assume a success count of 9 (k 5 9) and
a trial total of 10 (n 5 10).
886 Wetzels, Lee, and Wagenmakers
A rate problem: The model file. A so-called model
file is used to implement the model into WinBUGS. The
model file for inferring q from an observed n and k looks
like this:
# observed wins k out of total games n
k ~ dbin(theta,n)
Density
model
{
# prior on the rate parameter theta
theta ~ dunif(0,1)
95%
}
The twiddles symbol (~ ) means “is distributed as.”
Because we use a uniform distribution between 0 and 1
as a prior on the rate parameter q, we write theta ~
dunif(0,1). This indicates that, a priori, each value of q
is equally likely. Furthermore, k is binomially distributed
with parameters q and n (i.e., k ~ dbin(theta,n)). Note
that dunif and dbin are two of the predefined distributions in WinBUGS. All the distributions that are predefined in WinBUGS are listed in the distributions section in the WinBUGS manual, which can be accessed by
clicking the help menu and selecting user manual (or by
pressing F1). The hash symbol (#) is used for comments.
The lines starting with this symbol are not executed by
WinBUGS.
Copy the text into an empty file and save it as “model_
rateproblemfunction.txt” in the directory from which you
want to work. There are now various ways in which to
proceed. One way is to work from within WinBUGS;
another way is to control WinBUGS by calling it from a
more general purpose program. Here, we use the statistical programming language R (R Development Core Team,
2009) to call WinBUGS, but widely used alternative research programming environments such as MATLAB are
also available (Lee & Wagenmakers, 2009).
A rate problem: The R script. The next step is to
construct an R script to call BlackBox from R.5 When
we run the script “rscript_rateproblemfunction.R,”
WinBUGS starts, the MCMC sampling is conducted,
WinBUGS closes, and we return to R. The object
that ­WinBUGS has returned to R is called “rateproblem,”
and this object contains all the information about the
Bayesian inference for q.
In particular, the “rateproblem” object contains a single sequence of consecutive draws from the posterior
distribution of q, a sequence that is generally known as
an MCMC chain. We use the samples from the MCMC
chain to estimate the posterior distribution of q. To arrive
at the posterior distribution, the samples are not plotted as
a time series but as a distribution. In order to estimate the
posterior distribution of q, we applied the standard density
estimator in R. Figure 1 shows that the mode of the distribution is very close to .90, just as we expected. The posterior distribution is relatively spread out over the parameter
space, and the 95% credible interval extends from .59 to
.98, indicating the uncertainty about the value of q. Had
we observed 900 wins out of a total of 1,000 games, the
posterior of q would be much more concentrated around
0
.5
1.0
θ
Figure 1. Posterior distribution of the rate parameter q after
9 wins out of 10 games have been observed. The dashed gray line
indicates the mode of the posterior distribution at q 5 .90. The
95% credible interval extends from .59 to .98.
the mode of .90, since our knowledge about the true value
of q would have greatly increased.
Example 2: ObservedPlus
In this section, we examine the rate problem again, but
now we change the variables. Suppose we learn that before
we observed the current data, 10 games had already been
played, resulting in a single win. To add this information,
we design a function that adds 1 to the number of observed
wins, and 10 to the number of total games. So, when we
use k 5 9 and n 5 10 as before, we end up with
and
knew 5 kold 1 1 5 9 1 1 5 10
(2)
nnew 5 nold 1 10 5 10 1 10 5 20.
(3)
Hence, when we use our new function, the mode of the
posterior distribution should no longer be .90 but .50
(10/20 5 .50). Of course, this particular problem does not
require the use of WBDev and could easily be handled
using plain WinBUGS code. It is the simplicity of the
present problem, however, that makes it suitable as an introductory WBDev example.
In order to apply WBDev to the problem above, we are
going to build a function called “ObservedPlus,” using
the template “VectorTemplate.odc.” This template is located in the folder “. . .\BlackBoxComponentBuilder1.5 \
WBdev \Mod.”
ObservedPlus: The WBDev script. The script file
“ObservedPlus.odc” shows text in three colors. The
parts that are colored black should not be changed. The
parts in red are comments, and these are not executed by
­BlackBox. The parts in blue are the most relevant parts of
Bayesian Inference Using WBDev 887
the code, because these are the parts that can be changed
to create the desired function.
The templates for coding the functions and distributions—written by David Lunn and Chris Jackson—come
bundled with the WBDev software. These templates support the development of new functions and distributions,
such that researchers can focus on the specific functions
they wish to implement without having to worry about
programming Component Pascal code from scratch.
We now give a detailed explanation of the ObservedPlus WBDev function. The numbers (*X*) correspond
to the numbers in the ObservedPlus WBDev script. For
this simple example, we show some crucial parts of the
WBDev scripts below.
(*1*) MODULE WBDevObservedPlus;
The name of the module is typed here. We have named
our module ObservedPlus. The name of the module (so
the part after MODULE WBDev . . .) must start with a
capital letter.
(*2*) args := "ss";
Here, we must define specific arguments about the
input of the function. We can choose between scalars (s) and vectors (v). A scalar means that the input is
a single number. When we want to use a variable that
consists of more numbers (e.g., a time series), we need
a vector. This line has to correspond with the constants
defined at (*3*). In our example, we use two scalars,
the number of successes k and the total number of observations n.
(*3*) in = 0; ik = 1;
Because of what has been defined at (*2*), WBDev
already knows that there should be two variables here.
We name them in and ik, with in at the first spot (with
number 0) and ik at the second spot (with number 1).
WBDev always starts counting at 0 and not at 1.
Note that we did not name our variables n and k, but
in and ik. This is because it is more insightful to use n
and k later on, and it is not possible to give two or more
variables the same name. Finally, note that the positions
of the constants correspond to the positions of the input
of the variables into the function in the model file. We
will return to this issue later.
(*4*) n, k: INTEGER;
The variables that are used in the calculations need to
be defined. Both variables are defined as integers, because the binomial distribution allows only integers as
input: Counts of successes and the total games that are
played can only be positive integers.
(*5*)n
k
:= SHORT(ENTIER(func.arguments[in]
[0].Value()));
:= SHORT(ENTIER(func.arguments[ik]
[0].Value()));
This code takes the input values (in and ik) and gives
them a name. We defined two variables in (*4*), and
we are now going to use them. What the script says here
is: Take the input values in and ik and store them in the
integer variables n and k. Because the input variables
are not automatically assumed to be integers, we have to
transform them and make sure the program recognizes
them as integers. So, in other words, the first line says
that n is the same as the first input variable of the function (see Figure 2), and the second line says that k is the
same as the second input variable of the function.
(*6*)
n:=n+10;
k:=k+1;
values[0] := n;
values[1] := k;
This is the part of the script where we do the actual
calculations. At the end of this part, we fill the output
array values with the new n and k.
(*7*) END WBDevObservedPlus.
Finally, we need to make sure that the name of the module at the end is the same as the name at the top of the
file. The last line has to end with a period. Hence, the
last line of the script is “ENDWBDevObservedPlus.”
Now we need to compile the function by pressing
“Ctrl1k.” Syntax errors cause WBDev to return an error
message. Name this file “ObservedPlus.odc” and save
it in the directory “. . .\BlackBoxComponentBuilder1.5\
WBdev\ Mod.”
Figure 2. Detailed explanation of part (*5*) of “ObservedPlus.odc.”
888 Wetzels, Lee, and Wagenmakers
model
{
# Uniform prior on the rate parameter
theta ~ dunif(0,1)
# use the function to get the new n and
the new k
data[1:2] <- ObservedPlus(n,k)
# define the new n and new k as
variables
newn <- data[1]
newk <- data[2]
# the new observed data
newk ~ dbin(theta,newn)
}
We assume a uniform prior on q (i.e., theta ~
dunif(0,1)). The function ObservedPlus takes as input
the total number of games n and the number of wins k.
From them, the new n and new k can be calculated (i.e.,
data[1:2] <- ObservedPlus(n,k)). Note that functions require the use of the assignment operator (<-) instead of the twiddles symbol (~). Remember that in the
WBDev function, the location of in was 0 and the location
of ik was 1. Because that order was used, the input has to
have n first and then k.
Next, newn is the first number in the vector data, and
newk is the second (i.e., newn <- data[1], newk <data[2]). Remember that when scripting in WBDev, the
first element has index 0, but in the model file, the first
element has index 1. Finally, we use our new variables
to do inference on the rate parameter q (i.e., newk ~
dbin(theta,newn)).
Copy the text from the model file into an empty text file
and name this file “model_observedplus.txt.” Copy this
file to the location of the model file that was used in the
rate problem example.
ObservedPlus: The R script. To run this model from
R, we can use the script of the original rate problem. The
only thing that needs to be changed is the name of the
model file. This should now be “model_observedplus.txt.”
Change this name and run the R script.
95%
Density
We are not entirely ready to use the function yet. WBDev
needs to know that there exists a function called ObservedPlus; WBDev also needs to know what the input looks
like (i.e., how many inputs are there, what order are they
presented in, and are they scalars and vectors?), and what
the output is. To accomplish this, open the file “functions
.odc” in the directory “. . .\BlackBoxComponentBuilder1.5\
WBdev\ Rsrc.” Add the line v<-"ObservedPlus"(s,s)
"WBDevObservedPlus.Install" and then save the
file. The next time that WBDev is started, it knows that
there is a function named ObservedPlus, which has two
scalars as input and a vector as output. The function is now
ready to be used in a model file.
ObservedPlus: The model file. In order to use the
newly scripted function ObservedPlus, we use a model
file that is similar to the model file used in the earlier rate
problem example.
0
.5
1.0
θ
Figure 3. Posterior distribution of the rate parameter q after
using the function ObservedPlus. The dashed gray line indicates
the mode of the posterior distribution at q 5 .50. The 95% credible interval extends from .30 to .70.
Figure 3 shows the posterior distribution of q. The mode
of the distribution is .50, because knew 5 10 and nnew 5
20. Again, because the total number of games played is
fairly small, the posterior distribution of q is relatively
spread out (the 95% credible interval ranges from .30 to
.70), reflecting our uncertainty about the true value of q.
Example 3: The EV Model
In the example described above, we could have used
plain WinBUGS code instead of writing a script in
­BlackBox. But sometimes it can be very useful to write
a BlackBox script instead of plain WinBUGS code, especially if the model under consideration is relatively
complex. Implementing such a model into WBDev can
speed up the computation time for inference substantially. The reason for this speedup is that WBDev scripts
are precompiled, whereas the WinBUGS model files are
interpreted at runtime. The present example, featuring the
EV model to understand risk-seeking behavior in decision making, provides a concrete demonstration of this
general point.
Suppose a psychologist wants to study decision making
of clinical populations under controlled conditions. A task
that is often used for this purpose is the Iowa gambling
task (IGT), developed by Bechara and Damasio (Bechara,
Damasio, Damasio, & Anderson, 1994; Bechara, Damasio,
Tranel, & Damasio, 1997).
In the IGT, participants have to discover, through trial
and error, the difference between risky and safe decisions.
In the computerized version of the IGT, the participant
starts with $2,000 in play money. The computer screen
shows players four decks of cards (A, B, C, and D), and
then they have to select a card from one of the decks. Each
Bayesian Inference Using WBDev 889
Table 1
Rewards and Losses in the Iowa Gambling Task
Bad Decks
Good Decks
A
B
C
D
Rewards per trial
100
100
50
50
Number of losses per 10 cards
5
1
5
1
Loss per 10 cards
1,250
1,250
250
250
Net profit per 10 cards
2250
2250
250
250
Note—Cards from Decks A and B yield higher rewards than do cards
from Decks C and D, but they also yield higher losses. The net profit is
highest for cards from Decks C and D.
card is associated with either a reward or a loss. The default payoff scheme is presented in Table 1.
At the start of the IGT, participants are told that they
should maximize net profit. During the task, they are presented with a running tally of the net profit, and the task
finishes after 250 card selections.
The EV model proposes that choice behavior in the IGT
comes about through the interaction of three latent psychological processes. Each of these processes is vital for
successful performance, typified by a gradual increase in
preference for the good decks over the bad decks. First,
the model assumes that the participant, after selecting a
card from deck k, k  {1, 2, 3, 4} on trial t, calculates the
resulting net profit or valence. This valence vk is a combination of the experienced reward W(t) and the experienced
loss L(t):
where c is the response consistency or sensitivity parameter. In fits to data, this parameter is usually constrained to
the interval [25, 5]. When c is positive, response consistency q increases (i.e., the temperature 1/q decreases) with
the number of observations. This means that choices will
be more and more guided by the expected valences. When
c is negative, choices will become more and more random
as the number of card selections increases.
In sum, the EV model decomposes choice behavior in
the IGT into three components or parameters: (1) an attention weight parameter w that quantifies the weighting
of losses versus rewards, (2) an updating rate parameter a
that quantifies the memory for rewards and losses, and
(3) a response consistency parameter c that quantifies the
level of exploration.
The EV model: The WBDev script. To implement the
EV model as a function in WBDev, it is useful to first describe what data are observed and passed on to WinBUGS.
In this example, we examine the data of 1 participant who
has completed a 250-trial IGT. Hence, the observed data
are an index of which deck was chosen at each trial and the
sequence of wins and losses that the participant incurred.
Please see the script file called “EV.odc.”
(*1*) We name our module EV.
(*2*) In the EV example, we use three scalars for the
three parameters and three vectors for the wins, losses,
and index at each trial.
(4)
(*3*) We start with the data vectors (the order is arbitrary
Thus, the first parameter of the EV model is w, the attention weight for losses relative to rewards, w  [0, 1].
On the basis of the sequence of valences vk experienced
in the past, the participant forms an expectation Evk of the
valence for deck k. In order to learn, new valences need
to update the expected valence Evk . If the experienced valence vk is higher or lower than expected, Evk needs to be
adjusted upward or downward, respectively. This intuition
is captured by the equation
(*4*) In this section, we define all the variables that we
vk (t) 5 (1 2 w)W(t) 1 wL(t).
Evk (t 1 1) 5 Evk (t) 1 a[vk (t) 2 Evk (t)],
(5)
in which the updating rate a  [0, 1] determines the impact of recently experienced valences.
The EV model also uses a reinforcement learning
method called softmax selection or Boltzmann exploration (Kaelbling, Littman, & Moore, 1996; Luce, 1959) to
account for the fact that participants initially explore the
decks and, only after a certain number of trials, decide to
always prefer the deck with the highest expected valence:
Pr  Sk (t + 1)  =
exp (θ (t ) Evk )
∑
4
exp
j =1
.
(θ (t ) Ev ) j
(6)
In this equation, 1/q(t) is the “temperature” at trial t and
Pr(Sk ) is the probability of selecting a card from deck k.
In the EV model, the temperature is assumed to vary with
the number of observations according to
q(t) 5 (t/10)c,
(7)
but needs to correspond to the one used in the model
file), and we name these constants iwins, ilosses,
and iindex. After that, the function has as input the
parameters of the EV model, iw, ia, and ic.
need to use in our calculations. Several mathematical
functions are already available in WBDev. Information
about these functions can be found by right-clicking
the word “Math” in the script and then by clicking
“documentation.”
(*5*) Here, we take our input EV parameters and assign
them to the variables that we defined in part (*4*).
(*6*) This is the part of the script where we do the actual
calculations. At the end of this part, we fill the output
variable called “values” with the output of our EV function, the probability of choice for a deck.
(*7*) Make sure that the name of the module at the end
is the same as the name at the top of the file. The last
line has to end with a period.
(*11*) The DrawSample(.) procedure returns a pseudo-
random number from the new distribution.
Name this file “EV.odc” and save it in the directory
“. . .\BlackBoxComponentBuilder1.5 \WBdev\ Mod.”
Open the file “functions.odc” in the directory “. . .\
­BlackBox Component Builder 1.5\WBdev\Rsrc.” Add the
line v <- "EV"(v,v,v,s,s,s) "WBDevEV.Install"
890 Wetzels, Lee, and Wagenmakers
and then save the file. The next time that WBDev is started,
it knows that there is a function named EV that has three
vectors and three scalars as input and a vector as output.
The EV model: The model file. In order to use the
EV model, we need to implement the graphical model
in ­WinBUGS. The following model file is used in this
example:
model
{
# EV parameters are assigned prior
distributions
w ~ dunif(0,1)
a ~ dunif(0,1)
c ~ dunif(-5,5)
# data from the EV function
evprobs[1:1000] <- EV(wi[],lo[],ind[],
w,a,c)
# only use the information from the
chosen deck
# see explanation below
for (i in 1:250)
{
p.EV[i,1] <- evprobs[deckA[i]]
p.EV[i,2] <- evprobs[deckB[i]]
p.EV[i,3] <- evprobs[deckC[i]]
p.EV[i,4] <- evprobs[deckD[i]]
ind[i] ~ dcat(p.EV[i,])
}
}
The parameters of the model, w, a, c, are assigned uniform prior distributions. w and a are bounded between
0 and 1, and c is bounded between 25 and 5 (i.e., w ~
dunif(0,1), a ~ dunif(0,1), c ~ dunif(-5,5)).
The wins and the losses from the 250 trials are stored in
the vectors wi and lo. The indices from the decks that were
chosen are stored in the vector ind. Together with the EV parameters, they are input for the EV function that calculates
the probability per choice (i.e., evprobs[1:1000] <EV(wi[],lo[],ind[],w,a,c)).
Note that this function calculates 1,000 probabilities
for a 250-trial data set, because the probability for each
deck is calculated not only for the chosen deck, but also
for all decks. So, at each trial, four probabilities are calculated, and for 250 trials, this totals 1,000 probabilities.
However, we are interested only in the probability of the
chosen deck.
To handle this problem, we make four vectors, deckA,
deckB, deckC, and deckD, which are rows of length 250.
Each vector contains a sequence of numbers where the
number at position t is calculated by adding four to the
number at position t21 (xt 5 xt21 1 4). The vector deckA
starts with number 1, deckB starts with number 2, deckC
starts with number 3, and deckD starts with number 4.
Using these vectors, we can disentangle the probabilities
for each deck at each trial; evprobs[deckA[i]] corresponds to the probabilities of choosing deck 1 at each
trial i, evprobs[deckB[i]] to the probabilities of
choosing deck 2 at each trial i, evprobs[deckC[i]] to
the probabilities of choosing deck 3 at each trial i, and
evprobs[deckD[i]] to the probabilities of choosing
deck 4 at each trial i.
Finally, we state that the choice for a deck at trial i (the
observed data vector ind) is categorically distributed (i.e.,
ind[i] ~ dcat(p.EV[i,])). The categorical distribution (which is a special case of the multinomial distribution)
is the probability distribution for the choice of a card deck.
This distribution is a generalization of the Bernoulli distribution for a categorical random variable (i.e., the choice for
one of the four decks at each trial of the IGT). Copy the text
from the model into an empty file and save it as “model_
ev.txt” in the directory from which you want to work.
The EV model: The R script. To run this model and
to supply WinBUGS with the data, we use the R script
called “rscript_expectancyvalence.r.” Change the working
directory (in the R script) to the directory where the model
file is located on the computer. This script contains fictitious data from a person who completed a 250-trial IGT.
Figure 4 shows that the posterior mode of the attention weight parameter w is .43, the posterior mode of the
update parameter a is .25, and the posterior mode of the
consistency parameter c is 0.58.
On an average computer, it takes about 85 sec to generate these posterior distributions. Had we used plain
­WinBUGS instead of WBDev code to compute these
distributions, the calculation time would have been ap-
95%
95%
Density
95%
0
.5
w
1.0
0
.5
a
1.0
–1
0
1
c
Figure 4. Posterior distributions of the three expectancy-valence parameters, w, a, and c. The dashed gray lines indicate the modes
of the posterior distributions at w 5 .43, a 5 .25, and c 5 0.58. The 95% credible intervals for w, a, and c extend from .38 to .57, from
.17 to .36, and from 0.31 to 0.74, respectively.
Bayesian Inference Using WBDev 891
proximately 15 min. Hence, implementing the function
into WBDev speeds up the analysis by a factor of 10.
Distributions
Statistical distributions are invaluable in psychological research. For example, in the simple rate problem discussed earlier, we use the binomial distribution to model
our data. WinBUGS comes equipped with an array of predefined distributions, but it does not include all distributions that are potentially useful for psychological modeling. Using WBDev, researchers can augment WinBUGS
to include these desired distributions.
The next section explains how to write a new distribution, starting with the binomial distribution as a simple
introduction, and then considering the more complicated
shifted Wald distribution.
Example 4: Binomial Distribution
The binomial distribution is already hard-coded in
­WinBUGS. But, because it is a very well-known and
relatively simple distribution, it serves as a useful first
example.
To program a distribution in WBDev, we can use the
distribution template that is already in the BlackBox directory. This file is located in the folder “. . .\BlackBoxComponentBuilder1.5\WBdev\Mod.” In order to program
the distribution, we first need to write out the log likelihood function:
log  Pr ( K = k )  = log ( f ( k ; n,θ ) )
 n 

= log   θ k (1 − θ )n − k 
 k 

 n
= log   + log(θ k ) + log(1 − θ )n − k
 k
= log( n!) − log( k !) − log( n − k )!
+ k log(θ ) + ( n − k ) log(1 − θ ). (8)
Binomial distribution: The WBDev script. Here, we
describe the WBDev script for the binomial distribution
(see the file “BinomialTest.odc”).
(*1*) We name our module BinomialTest.
(*2*) The parameters of the input of the binomial distri-
bution, q and n.
(*3*) Here, global variables can be declared. By global
is meant that it is loaded only once, whereas the value
of the variable may be needed many times. This part
of the template does not need to be changed for this
example.
(*4*) We have to declare what type of arguments are the
input of the distribution. In this case, these are two scalars (i.e., two single numbers), q and n.
The other thing that is defined in this part of the script
is whether the cumulative distribution is to be provided.
If so, canIntegrate should be set to true. If this is set to
true, an algorithm should be provided at (*11*). We
set canIntegrate to false because we did not implement
the cumulative distribution.
(*6*) This part of the code should define the natural
bounds of the distribution. In our case, we take 0 as a
lower bound and n as an upper bound, because k can
never be larger than n.
(*7*) As the name implies, this is the part where the
full log likelihood of the distribution is defined. This
is an implementation of the log likelihood as defined
in Equation 8.
(*8*) Sometimes WinBUGS can ignore the normaliz-
ing constants. When that is the case, WinBUGS calls
­LogPropLikelihood(.). In our example, we refer back
to the full log likelihood function.
(*9*) Occasionally, WinBUGS can make use of the Log-
Prior(.) procedure, which is proportional to the real logprior function. In other words, this procedure omits the
additive constants on the log scale. In our example, we
just refer back to the full log likelihood function.
(*10*) This is the part where the cumulative distribution
is defined when in part (*7*) canIntegrate is set to
true.
Because we set this to
anything in this section.
we do not define
(*11*) The DrawSample(.) procedure returns a pseudo-
random number from the new distribution.
(*12*) The last thing that needs to be done is to make
sure that the name of the module at the end is the same
as the name at the top of the file. The last line has to
end with a period.
Save this file as “BinomialTest.odc” and copy this
file into the appropriate BlackBox directory, “. . .\
BlackBoxComponentBuilder1.5\WBdev\Mod.”
Open the distribution file “distributions.odc” in the
directory “. . .\BlackBoxComponent Builder 1.5\WBdev\
Rsrc.” Add the line s ~ "BinomialTest"(s,s)
"­
WBDevBinomialTest.Install" and then save it.
Binomial distribution: The model file. To use the
scripted binomial distribution, we write a model file that
is very similar to the model file used in the rate problem
example. We only need to change the name of the distribution from dbin to BinomialTest.
model
{
# prior on rate parameter theta
theta~dunif(0,1)
# observed wins k out of total games n
k~BinomialTest(theta,n)
(*5*) This describes whether the distribution is dis-
crete or continuous. When the distribution is discrete,
­isDiscrete should be set to true. When the distribution
is continuous, it should be set to false. For the binomial
distribution, isDiscrete is set to true.
false,
# compute the posterior predictive of k
postpred.k~BinomialTest(theta,n)
}
This example is essentially the same statistical problem as the first example, the rate problem. Ten games
are played (i.e., n 5 10), and nine games are won (i.e.,
k 5 9). We assume a uniform prior on q (i.e., theta ~
dunif(0,1)). The observed wins k are distributed as
our newly made BinomialTest with rate parameter q
and total games n (i.e., k~BinomialTest(theta,n)).
With q and k defined, this completes the model for
­BinomialTest. The Drawsample feature of the function
([(*11*)]) produces the posterior predictive values for
k (i.e., ­postpred.k~BinomialTest(theta,n)). Save
this file as “model_rateproblemdistribution.txt” and copy
it to the working directory.
Binomial distribution: The R script. The last thing
that we need to do is to start R and open the appropriate R script “rscript_rateproblemdistribution.r.” Change
the working directory (in the R script) to the directory
in which the model file is located on the computer. After
running the code, the results should be similar to those
shown in Figure 1.
After you have observed those data, the prediction of
future data can be of interest. The so-called posterior predictive distribution gives the relative probability of different outcomes after the data have been observed. First,
a sample is drawn from the joint posterior distribution.
Next, data are generated using the posterior sample.
In this example, these different outcomes can be k 5
1, 2, . . . , 10. The posterior predictive is often used for
checking the assumptions of a model. If a model describes
the data well, the posterior predictive generated under the
model should resemble the observed data. Large differences between the observed data and the posterior predictive imply that the model is not suitable for the data at
hand. Figure 5 shows the posterior predictive of k. The
median of the posterior predictive is k 5 9, which corresponds to the observed data.
Example 5: Shifted Wald Distribution
Many psychological models use RTs to infer latent
psychological properties and processes (Luce, 1986).
One common distribution used to model RTs is the inverse Gaussian or Wald distribution (Wald, 1947). This
distribution represents the density of the first passage
times of a Wiener diffusion process toward a single
absorbing boundary, as shown in Figure 6, using three
parameters.
The parameter v reflects the drift rate of the diffusion
process. The parameter a reflects the separation between
the starting point of the diffusion process and the absorbing barrier. The third parameter, Ter , is a positive-valued
parameter that shifts the entire distribution. The probability density function for this shifted Wald distribution is
given by
f ( t | v, a, Ter ) =
a
  a − v (t − T )2

er 
exp − 
2 ( t − Ter )



 , (9)

2π ( t − Ter )
which is unimodel and positively skewed. Because of these
qualitative properties, it is a good candidate for fitting em3
Probability
892 Wetzels, Lee, and Wagenmakers
0
1
2
3
4
5
6
7
8
9
10
k
Figure 5. Posterior predictive of k, the number of wins out of
10 games. The median of the posterior predictive is k 5 9.
Wald distribution
a
v
Ter
Time
Figure 6. Diffusion process with one boundary. The shifted
Wald parameter a reflects the separation between the starting
point of the diffusion process and the absorbing barrier, v reflects
the drift rate of the diffusion process, and Ter is a positive-valued
parameter that shifts the entire distribution.
pirical RT distributions. As an illustration, Figure 7 shows
changes in the shape of the shifted Wald distribution as
a result of changes in the shifted Wald parameters v, a,
and Ter .
The shifted Wald parameters have a clear psychological interpretation (e.g., Heathcote, 2004; Luce, 1986;
Schwarz, 2001, 2002). Participants are assumed to accumulate noisy information until a predefined threshold
amount is reached and a response is initiated. Drift rate v
quantifies task difficulty or participant ability, response
criterion a quantifies response caution, and the shift parameter Ter quantifies the time needed for nondecision
processes (Matzke & Wagenmakers, 2009). Experimental
paradigms in psychology for which it is likely that there
Bayesian Inference Using WBDev 893
1
3
2
1
0
0
1
2
3
4
t
1
2
3
4
0
0
t
2
1
0
0
v = 3.8
a=1
Ter = 0
3
2
1
0
4
v=2
a=1
Ter = 0.8
f(t)
2
4
v=2
a = 2.5
Ter = 0
3
f(t)
3
f(t)
4
v=2
a=1
Ter = 0
f(t)
4
1
2
3
4
t
0
1
2
3
4
t
Figure 7. Changes in the shape of the shifted Wald distribution as a result of changes in the parameters v, a, and Ter. Each panel shows
the shifted Wald distribution with different combinations of parameters.
is only a single absorbing boundary include saccadic eye
movement tasks with few errors (Carpenter & Williams,
1995), go/no-go tasks (Gomez, Ratcliff, & Perea, 2007),
or simple RT tasks (Luce, 1986, pp. 51–57). Here, we
show how to implement the shifted Wald distribution in
WBDev.
Shifted Wald distribution: The WBDev script.
Code for the WBDev script is available at the first author’s
Web site. Open BlackBox, and open the file “ShiftedWald
.odc.”
(*1*) We name our module ShiftedWald.
(*2*) The parameters of the distribution, which, in this
case, are the drift rate v, response caution a, and shift
Ter.
(*4*) We have to declare what type of arguments are the
input of the distribution. In this case, these are the three
scalar parameters of the shifted Wald distribution.
(*6*) This part of the code should define the natural
bounds of the distribution. In our case, we take Ter as
a lower bound and INF (meaning 1) as an upper
bound. Save this file as “ShiftedWald.odc” and copy
this file into the appropriate BlackBox directory, “. . .\
BlackBoxComponentBuilder1.5\WBdev\Mod.”
Open the distribution file “distributions.odc” in the directory “. . .\BlackBox Component Builder 1.5\WBdev\
Rsrc.” Add the line s ~ "ShiftedWald"(s,s,s)
"­
WBDevShiftedWald.Install" and then save it.
Shifted Wald distribution: The model file. Once we
have implemented the WBDev function in BlackBox, we
can use the function ShiftedWald in the model. The model
file is as follows:
model
{
# prior distributions for shifted Wald
parameters
# drift rate
v ~ dunif(0,10)
# boundary separation
a ~ dunif(0,10)
# Non-decision time
Ter ~ dunif(0,1)
# data are shifted Wald distributed
for (i in 1:nrt)
{
rt[i] ~ ShiftedWald(v,a,Ter)
}
}
The priors for v and a are uniform distributions that
range from 0 to 10 (i.e., v ~ dunif(0,10), i.e., a ~
dunif(0,10)). The prior for Ter is a uniform distribution that ranges from 0 to 1 (i.e., Ter ~ dunif(0,1)).
With the priors in place, we can use our ShiftedWald
function to estimate the posterior distributions for the
three model parameters v, a, and Ter (i.e., rt[i] ~
ShiftedWald(v,a,Ter)). Save the lines as a text file
and name it “model_shiftedwaldind.txt.”
Shifted Wald distribution: The R script. Now, open
the R script “rscript_shiftedwald_individual.r” for the
individual analysis into an R file and run it. Change the
directory of the location of the model file and the location
of the copy of BlackBox to the appropriate directories.
The R script loads a real data set from a lexical decision
task (Wagenmakers, Ratcliff, Gomez, & McKoon, 2008).
Nineteen participants had to quickly decide whether a visually presented letter string was a word (e.g., table) or
a nonword (e.g., drapa). We will fit the RTs of correct
“word” responses of the first participant to the shifted
Wald distribution. The RT data can be downloaded from
www.ruudwetzels.com.
Figure 8 shows the posterior distribution of the three
shifted Wald parameters, v, a, and Ter . One thing that
stands out is that the posterior distributions of the shifted
Wald parameters are very spread out across the parameter space. The 95% credible intervals for v, a, and Ter
extend from 4.12 to 8.00, from 0.80 to 3.52, and from
.09 to .36, respectively. It seems that data from only
1 participant are not enough to yield very accurate estimates of the shifted Wald parameters. In the following
section, we show how our estimates will improve when
we use a hierarchical model and analyze all participants
simultaneously.
894 Wetzels, Lee, and Wagenmakers
95%
95%
Density
95%
3
6
9
0
v
2
4
a
0
.25
.50
Ter
Figure 8. Posterior distribution of the three Wald parameters v, a, and Ter. The dashed gray lines indicate the modes of the posterior
distributions at v 5 5.57, a 5 1.09, and Ter 5 .33. The 95% credible intervals for v, a, and Ter extend from 4.12 to 8.00, from 0.80 to 3.52,
and from .09 to .36, respectively.
Shifted Wald distribution: A hierarchical extension. In an experimental setting, the problem of few data
per participant can be addressed by hierarchical modeling
(Farrell & Ludwig, 2008; Gelman & Hill, 2007; Rouder,
Sun, Speckman, Lu, & Zhou, 2003; Shiffrin, Lee, Wagenmakers, & Kim, 2008). In our shifted Wald example, each
participant is assumed to generate their data according to
the shifted Wald distribution, but with different parameter values. We extend the individual analysis and assume
that the parameters for each participant are governed by a
group normal distribution. This means that all individual
participants are assumed to have their shifted Wald parameters drawn from the same group distribution, allowing
the data from all the participants to be used for inference,
without making the unrealistic assumption that the participants are identical copies of each other.
The model file that implements the hierarchical shifted
Wald analysis is shown below:
model
{
# prior distributions for group means:
v.g ~ dunif(0,10)
a.g ~ dunif(0,10)
Ter.g ~ dunif(0,1)
# prior distributions for group standard
deviations:
sd.v.g ~ dunif(0,5)
sd.a.g ~ dunif(0,5)
sd.Ter.g ~ dunif(0,1)
# transformation from group standard
deviations to group
# precisions (i.e., 1/var, which is
what WinBUGS expects
# as input to the dnorm distribution):
lambda.v.g <- pow(sd.v.g,-2)
lambda.a.g <- pow(sd.a.g,-2)
lambda.Ter.g <- pow(sd.Ter.g,-2)
# data come from a shifted Wald
distribution
for (i in 1:ns) #subject loop
{
# individual parameters drawn from
group level
# normals censored to be positive
using the
# I(0,) command:
v.i[i] ~ dnorm(v.g,lambda.v.g)I(0,)
a.i[i] ~ dnorm(a.g,lambda.a.g)I(0,)
Ter.i[i] ~ dnorm(Ter.g,lambda.Ter.g)
I(0,)
# for each participant,
# data are shifted Wald distributed
for (j in 1:nrt[i])
{
rt[i,j] ~ ShiftedWald(v.i[i],
a.i[i],Ter.i[i])
}
}
}
The hierarchical analysis of the RT data proceeds as
follows. The prior for the group means is a uniform distribution, ranging from 0 to 10 (i.e., v.g ~ dunif(0,10),
a.g ~ dunif(0,10)) or from 0 to 1 (i.e., Ter.g ~
dunif(0,1) ). The standard deviations are drawn
from a uniform distribution ranging from 0 to 5 (i.e.,
sd.v.g ~ dunif(0,5), sd.a.g ~ dunif(0,5))
or from 0 to 1 (i.e., sd.Ter.g ~ dunif(0,5)). Next,
the standard deviations have to be transformed to precisions (i.e., lambda.v.g <- pow(sd.v.g,-2) ,
lambda.a.g <- pow(sd.a.g,-2) , lambda
.Ter.g <- pow(sd.Ter.g,-2)). Then, the individual
parameters v.i, a.i, and Ter.i are drawn from normal distributions with corresponding group means and group
precisions (i.e., v.i[i] ~ dnorm(v.g,lambda.v.g)
I(0,), a.i[i] ~ dnorm(a.g, lambda.a.g)I(0,),
Ter.i[i] ~ dnorm(Ter.g, lambda.Ter.g)I(0,)).
The I(0,) command indicates that the distribution is leftcensored at 0. For each individual, the data are distributed
according to a shifted Wald distribution with their own
individual parameters. Save the model file as a text file
and name it “model_shiftedwaldhier.txt.”
When we run this model using the R script for the hierarchical analysis, we first focus on the group mean pa-
Bayesian Inference Using WBDev 895
rameters v.g, a.g, and Ter .g. Figure 9 shows the posterior
distributions of the shifted Wald group mean parameters.
The distributions indicate that there is relatively little uncertainty about the parameter values. The posterior distributions of the group mean parameters are concentrated
around their modes v.g 5 4.27, a.g 5 0.97, and Ter .g 5
.36. The 95% credible intervals for v.g, a.g, and Ter .g extend from 3.80 to 4.70, from 0.85 to 1.10, and from .34 to
.38, respectively.
It is informative to consider the influence of the hierarchical extension on the individual estimates for the shifted
Wald parameters. Specifically, we can examine the posterior distributions for the same participant that we analyzed
in the individual shifted Wald analysis, but now in the hierarchical setting.
The hierarchical extension leads to a practical improvement, through a speedup of the MCMC estimation process.
However, the hierarchical extension also leads to a theoretical improvement, because, as compared with the individual analysis, the posterior distributions appear much
less spread out. This shows that the hierarchical model
leads to a better understanding of the model parameters.
To underscore this point, Figure 10 shows the posterior distributions of the individual shifted Wald param-
eters, for both the hierarchical analysis and the individual
analysis. It is clear that the posterior distributions of the
shifted Wald parameters are less spread out in the hierarchical analysis than in the individual analysis. Also, the
parameter estimates from the hierarchical analysis are
slightly different from those from the individual analysis.
In particular, they seem to have moved toward their common group mean. This effect is called shrinkage and is a
standard and important property of hierarchical models
(Gelman, Carlin, Stern, & Rubin, 2004).
In sum, the WBDev implementation of the shifted Wald
distribution enables researchers to infer shifted Wald parameters from RT data. Not only does WinBUGS allow
straightforward analyses on individual data, it also makes
it easy to add hierarchical structure to the model (Lee,
2008; Rouder & Lu, 2005). This can greatly improve the
quality of the posterior estimates and is often a very sensible and informative way of analyzing data.
Discussion
In this article, we have shown how the WinBUGS Development Interface (WBDev) can be used to help psychological scientists model their sparse, noisy, but richly
95%
95%
Density
95%
3
6
9
0
2
v.g
4
0
a.g
.25
.50
Ter .g
Figure 9. Posterior distribution of the three “group-level” shifted Wald parameters v.g, a.g, and Ter .g. The dashed gray lines indicate
the modes of the posterior distributions at v.g 5 4.27, a.g 5 .97, and Ter .g 5 .36. The 95% credible intervals for v.g, a.g, and Ter .g extend
from 3.80 to 4.70, from 0.85 to 1.10, and from .34 to .38, respectively.
95%
95%
95%
95%
95%
Density
95%
3
6
v.i [1]
9
0
2
a.i [1]
4
0
.25
.50
Ter .i [1]
Figure 10. Posterior distribution of the three individual shifted Wald parameters v.i, a.i, and Ter .i from the hierarchical analysis
(solid lines) and the individual analysis (dotted lines). The dashed gray lines indicate the modes of the posterior distributions from the
hierarchical analysis at v.i[1] 5 4.57, a.i[1] 5 0.96, and Ter .i[1] 5 .34. The 95% credible intervals in the hierarchical model for v.i[1],
a.i[1], and Ter .i[1] extend from 3.86 to 5.49, from 0.75 to 1.24, and from .31 to .37, respectively.
896 Wetzels, Lee, and Wagenmakers
structured data. We have shown how a relatively complex
function such as the EV model can be incorporated into
a fully Bayesian analysis of data. Furthermore, we have
shown how to implement statistical distributions, such as
the shifted Wald distribution, that have specific application in psychological modeling but are not part of a standard set of statistical distributions.
The WBDev program is set up for Bayesian modeling
and is equipped with modern sampling techniques such as
MCMC. These sampling techniques allow researchers to
construct quantitative Bayesian models that are nonlinear,
highly structured, and potentially very complicated. The
advantages of using WBDev together with WinBUGS are
substantial. WinBUGS code can sometimes lead to slow
computation, and complex models might not work at all.
Scripting some components of the model in WBDev can
considerably speed up computation time. Furthermore,
compartmentalizing the scripts can make the model easier
to understand and debug. Moreover, WinBUGS facilitates
statistical communication between researchers who are
interested in the same model. The most basic advantage,
however, is that WBDev allows the user to program functions and distributions that are simply unavailable in
WinBUGS.
It should be mentioned that WinBUGS has an opensource alternative called OpenBUGS6 (Thomas, O’Hara,
Ligges, & Sturtz, 2006), a program that is also written
in Component Pascal and also uses the BlackBox framework. Because OpenBUGS is open-source, users are allowed to add new functions and distributions. At the time
of writing, OpenBUGS is supported by many researchers
who continually adjust and improve the program; hence,
OpenBUGS is a promising alternative to WinBUGS and
WBDev. In order to take full advantage of the added flexibility of OpenBUGS, however, the user needs to have substantial knowledge of both Component Pascal and Bayesian inference. By contrast, the WinBUGS and WBDev
environment is more restricted, and this limitation makes
it relatively easy for users to develop their own functions
and distributions.
Once a core psychological model is implemented
through WBDev, it is straightforward to take into account
variability across participants or items, using a hierarchical, multilevel extension (i.e., models with random effects for participants or items). This approach allows a
researcher to model individual differences as smooth variations in parameters of a certain cognitive model, reaching
an optimal compromise between the extremes of complete
pooling (i.e., treating all participants as identical copies)
and complete independence (i.e., treating each participant
as a fully independent unit). In general, statistical models
that are implemented in WinBUGS can be easily implemented to deal with the complexities that plague empirical
data, including cases in which data are missing, the sampling plan is unclear, or participants originate from different subgroups. For these reasons, we believe that the fully
Bayesian analysis of highly structured models is likely to
be a driving force behind future theoretical and empirical
progress in the psychological sciences.
Author Note
Correspondence concerning this article should be addressed to
R. Wetzels, Department of Psychology, University of Amsterdam,
­Roetersstraat 15, 1018 WB Amsterdam, The Netherlands (e-mail: wetzels
[email protected]).
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Notes
1. More information can be found at http://en.wikipedia.org/wiki/
Component_Pascal.
2. There already exist two concise tutorials on how to write functions
and distributions in WBDev, written by David Lunn and Chris Jackson.
The examples in those tutorials require advanced programming skills,
and they are not directly relevant for psychologists.
3. At the time of writing, all programs are available without charge.
Note that these programs work only under the Microsoft Windows operating system.
4. On the Windows Vista operating system, install the program in the
directory c:/WinBUGS14.
5. All the scripts can be found on the Web site of the first author: www
.ruudwetzels.com.
6. www.mathstat.helsinki.fi/openbugs/.
(Manuscript received June 16, 2009;
revision accepted for publication January 24, 2010.)
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