Computational modeling of cognition and behavior
Material type: TextPublication details: Cambridge University Press 2018 LondonDescription: xxii, 461p. With indexISBN:- 9781107525610
- 153.015118 F2C6
Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds | |
---|---|---|---|---|---|---|---|---|
Book | Ahmedabad General Stacks | Non-fiction | 153.015118 F2C6 (Browse shelf(Opens below)) | Available | 198960 |
Browsing Ahmedabad shelves, Shelving location: General Stacks, Collection: Non-fiction Close shelf browser (Hides shelf browser)
Table of content
Part I - Introduction to Modeling
1 - Introduction
2 - From Words to Models
Part II - Parameter Estimation
3 - Basic Parameter Estimation Techniques
4 - Maximum Likelihood Parameter Estimation
5 - Combining Information from Multiple Participants
6 - Bayesian Parameter Estimation
7 - Bayesian Parameter Estimation
8 - Bayesian Parameter Estimation
9 - Multilevel or Hierarchical Modeling
Part III - Model Comparison
10 - Model Comparison
11 - Bayesian Model Comparison Using Bayes Factors
Part IV - Models in Psychology
12 - Using Models in Psychology
13 - Neural Network Models
14 - Models of Choice Response Time
15 - Models in Neuroscience
Appendix A - Greek Symbols
Appendix B - Mathematical Terminology
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.
https://www.cambridge.org/core/books/computational-modeling-of-cognition-and-behavior/A4A90098E7CB9A58E5D030F408639D04#fndtn-information
There are no comments on this title.