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 Alzheimerâ€™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

In [1]:

```
# Python Environment and library version
%load_ext watermark
%watermark -v -m -p pymc3,theano,scipy,numpy,pandas,matplotlib,seaborn
```