| 书目名称 | Likelihood-Free Methods for Cognitive Science |
| 编辑 | James J. Palestro,Per B. Sederberg,Brandon M. Turn |
| 视频video | |
| 概述 | Provides an in-depth foundation of approximate Bayesian analysis (ABC).Offers a tutorial demonstration of a popular model in cognitive science that can be readily adapted to other models.Reviews many |
| 丛书名称 | Computational Approaches to Cognition and Perception |
| 图书封面 |  |
| 描述 | .This book explains the foundation of approximate Bayesian computation (ABC), an approach to Bayesian inference that does not require the specification of a likelihood function. As a result, ABC can be used to estimate posterior distributions of parameters for simulation-based models. Simulation-based models are now very popular in cognitive science, as are Bayesian methods for performing parameter inference. As such, the recent developments of likelihood-free techniques are an important advancement for the field...Chapters discuss the philosophy of Bayesian inference as well as provide several algorithms for performing ABC. Chapters also apply some of the algorithms in a tutorial fashion, with one specific application to the Minerva 2 model. In addition, the book discusses several applications of ABC methodology to recent problems in cognitive science...Likelihood-Free Methods for Cognitive Science .will be of interest to researchers and graduate students working in experimental, applied, and cognitive science. . |
| 出版日期 | Book 2018 |
| 关键词 | Likelihood-free Bayesian analysis; Approximate Bayesian computation; Minerva 2; Tutorial; Model Estimati |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-72425-6 |
| isbn_softcover | 978-3-319-89181-1 |
| isbn_ebook | 978-3-319-72425-6Series ISSN 2510-1889 Series E-ISSN 2510-1897 |
| issn_series | 2510-1889 |
| copyright | Springer International Publishing AG 2018 |
发表于 2025-3-21 19:00:25
