Suggested further readings

There are a number of different logical positions in which treatments of Bayesian statistics started that are relevant to NMA.

Statistics:

MacKay, D. J. C. (2003). Information theory, inference and learning algorithms. Cambridge university press. url: inference.org.uk/itprnn. Note: Beautiful combination of Bayesian statistics with information theory by the late David MacKay. The book is available for free online. Simply beautiful.

Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995). Bayesian data analysis. Chapman and Hall/CRC. Note: Still the standard book. Not exactly perfect for beginners but beautiful.

McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC. Note: A great text book full of intuitive illustration. This book also comes with youtube video/lectures that cover every chapter of the book: youtube.com/watch. One of the best resources out there to get started on Bayesian stuff.

Downey, A. (2013). Think Bayes: Bayesian Statistics in Python. O’Reilly Media, Inc. Note: This book is good for the python Bayes codes.

Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press. Note: This book is good for the python Bayes codes.

Normative Models:

Knill, D. C., and Richards, W. (Eds.). (1996). Perception as Bayesian inference. Cambridge University Press. Note: The book that propelled the field to visibility.

Welchman, A. E., Trommershauser, J., Kording, K., and Landy, M. S. (2011). Decoding the cortical representation of depth. Sensory cue integration. Oxford University Press. Note: This book is largely focused on Bayesian approaches to cue combination.

Analysis of neural data:

Kass, R. E., Eden, U. T., & Brown, E. N. (2014). Analysis of neural data (Vol. 491). New York: Springer. Note: This book contains a good treatment of Bayesian approaches to the analysis of neural data.