Suggested further readings¶
There are a number of different logical positions in which treatments of Bayesian statistics started that are relevant to NMA.
Beautiful combination of Bayesian statistics with information theory by the late David MacKay. the book is available for free online. Simply beautiful: MacKay, David JC, and David JC Mac Kay. Information theory, inference and learning algorithms. Cambridge university press, 2003.
Still the standard book. Not exactly perfect for beginners but beautiful: Gelman, Andrew, et al. Bayesian data analysis . CRC press, 2013.
A great text book full of intuitive illustration: McElreath, Richard. Statistical rethinking: A Bayesian course with examples in R and Stan, CRC press, 2020. This book also comes with youtube video/lectures that cover every chapter of the book https://www.youtube.com/watch?v=4WVelCswXo4&list=PLDcUM9US4XdNM4Edgs7weiyIguLSToZRI One of the best resources out there to get started on Bayesian stuff.
This book is good for the python Bayes codes: Downey, Allen. Think Bayes: Bayesian statistics in python . ” O’Reilly Media, Inc.”, 2013.
Another introductory book: Kruschke, John. Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan, Academic Press, 2014.
The book that propelled the field to visibility: Knill, David C., and Whitman Richards, eds. Perception as Bayesian inference . Cambridge University Press, 1996.
This book is largely focused on Bayesian approaches to cue combination: Trommershauser, Julia, Konrad Kording, and Michael S. Landy, eds. Sensory cue integration . Oxford University Press, 2011
Analysis of neural data:¶
This book contains a good treatment of Bayesian approaches to the analysis of neural data: Kass, Robert E., Uri T. Eden, and Emery N. Brown. Analysis of neural data . Vol. 491. New York: Springer, 2014.