PyMC3 port of Lee and Wagenmakers' Bayesian Cognitive Modeling - A Practical Course
All the codes are in jupyter notebook with the model explain in distributions (as in the book). Background information of the models please consult the book. You can also compare the result with the original code associated with the book (WinBUGS and JAGS; Stan)
All the codes are currently tested under PyMC3 v3.2 with theano 0.10.0.dev
3.1 Inferring a rate
3.2 Difference between two rates
3.3 Inferring a common rate
3.4 Prior and posterior prediction
3.5 Posterior prediction
3.6 Joint distributions
4.1 Inferring a mean and standard deviation
4.2 The seven scientists
4.3 Repeated measurement of IQ
5.1 Pearson correlation
5.2 Pearson correlation with uncertainty
5.3 The kappa coefficient of agreement
5.4 Change detection in time series data
5.5 Censored data
5.6 Recapturing planes
6.1 Exam scores
6.2 Exam scores with individual differences
6.3 Twenty questions
6.4 The two-country quiz
6.5 Assessment of malingering
6.6 Individual differences in malingering
6.7 Alzheimer’s recall test cheating
8.1 One-sample comparison
8.2 Order-restricted one-sample comparison
8.3 Two-sample comparison
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
11.1 Signal detection theory
11.2 Hierarchical signal detection theory
11.3 Parameter expansion
12.1 Psychophysical functions
12.2 Psychophysical functions under contamination
13.1 Evidence for optional stopping
13.2 Evidence for differences in ability
13.3 Evidence for the impact of extraversion
14.1 Multinomial processing model of pair-clustering
14.2 Latent-trait MPT model
15.1 The SIMPLE model
15.2 A hierarchical extension of SIMPLE
16.1 The BART model
16.2 A hierarchical extension of the BART model
17.1 The GCM model
17.2 Individual differences in the GCM
17.3 Latent groups in the GCM
18.1 Take-the-best
18.2 Stopping
18.3 Searching
18.4 Searching and stopping
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
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# Python Environment and library version
%load_ext watermark
%watermark -v -m -p pymc3,theano,scipy,numpy,pandas,matplotlib,seaborn