Description
Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.
Chapter
2 Getting started with WinBUGS
2.1 Installing WinBUGS, Matbugs, R, and R2WinBugs
2.2 Using the applications
2.3 Online help, other software, and useful URLs
Part II Parameter estimation
3 Inferences with binomials
3.2 Difference between two rates
3.3 Inferring a common rate
3.4 Prior and posterior prediction
4 Inferences with Gaussians
4.1 Inferring a mean and standard deviation
4.3 Repeated measurement of IQ
5 Some examples of data analysis
5.2 Pearson correlation with uncertainty
5.3 The kappa coefficient of agreement
5.4 Change detection in time series data
6.2 Exam scores with individual differences
6.5 Assessment of malingering
6.6 Individual differences in malingering
6.7 Alzheimer’s recall test cheating
7 Bayesian model comparison
7.3 Posterior model probabilities
7.4 Advantages of the Bayesian approach
7.5 Challenges for the Bayesian approach
7.6 The Savage–
Dickey method
7.7 Disclaimer and summary
8 Comparing Gaussian means
8.1 One-sample comparison
8.2 Order-restricted one-sample comparison
8.3 Two-sample comparison
9 Comparing binomial rates
9.1 Equality of proportions
9.2 Order-restricted equality of proportions
9.3 Comparing within-subject proportions
9.4 Comparing between-subject proportions
9.5 Order-restricted between-subjects comparison
10.1 No individual differences
10.2 Full individual differences
10.3 Structured individual differences
11 Signal detection theory
11.1 Signal detection theory
11.2 Hierarchical signal detection theory
12 Psychophysical functions
12.1 Psychophysical functions
12.2 Psychophysical functions under contamination
13 Extrasensory perception
13.1 Evidence for optional stopping
13.2 Evidence for differences in ability
13.3 Evidence for the impact of extraversion
14 Multinomial processing trees
14.1 Multinomial processing model of pair-clustering
14.2 Latent-trait MPT model
15 The SIMPLE model of memory
15.2 A hierarchical extension of SIMPLE
16 The BART model of risk taking
16.2 A hierarchical extension of the BART model
17 The GCM model of categorization
17.2 Individual differences in the GCM
17.3 Latent groups in the GCM
18 Heuristic decision-making
18.4 Searching and stopping
19 Number concept development
19.1 Knower-level model for Give-N
19.2 Knower-level model for Fast-Cards
19.3 Knower-level model for Give-N and Fast-Cards
Bayesian Cognitive Modeling
：A Practical Course
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