Thursday, October 04, 2018

2018-10-04 Thursday - Computation Modeling, Two Suggested Books

Based on the reviews, table of contents, etc. - I've ordered the two following books:


Computational Modeling of Cognition and Behavior
by Simon Farrell (Author), Stephan Lewandowsky
https://www.amazon.com/Computational-Modeling-Cognition-Behavior-Farrell/dp/110710999X/

"Computational modeling is now ubiquitous in psychology, and researchers who are not modelers may find it increasingly difficult to follow the theoretical developments in their field. This book presents an integrated framework for the development and application of models in psychology and related disciplines. Researchers and students are given the knowledge and tools to interpret models published in their area, as well as to develop, fit, and test their own models. Both the development of models and key features of any model are covered, as are the applications of models in a variety of domains across the behavioural sciences. A number of chapters are devoted to fitting models using maximum likelihood and Bayesian estimation, including fitting hierarchical and mixture models. Model comparison is described as a core philosophy of scientific inference, and the use of models to understand theories and advance scientific discourse is explained."

Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan 2nd Edition
https://www.amazon.com/Doing-Bayesian-Data-Analysis-Tutorial/dp/0124058884/

"Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets."

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