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Titlebook: Likelihood-Free Methods for Cognitive Science; James J. Palestro,Per B. Sederberg,Brandon M. Turn Book 2018 Springer International Publish

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發(fā)表于 2025-3-21 19:00:25 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Likelihood-Free Methods for Cognitive Science
編輯James J. Palestro,Per B. Sederberg,Brandon M. Turn
視頻videohttp://file.papertrans.cn/587/586128/586128.mp4
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
叢書名稱Computational Approaches to Cognition and Perception
圖書封面Titlebook: Likelihood-Free Methods for Cognitive Science;  James J. Palestro,Per B. Sederberg,Brandon M. Turn Book 2018 Springer International Publish
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
關(guān)鍵詞Likelihood-free Bayesian analysis; Approximate Bayesian computation; Minerva 2; Tutorial; Model Estimati
版次1
doihttps://doi.org/10.1007/978-3-319-72425-6
isbn_softcover978-3-319-89181-1
isbn_ebook978-3-319-72425-6Series ISSN 2510-1889 Series E-ISSN 2510-1897
issn_series 2510-1889
copyrightSpringer International Publishing AG 2018
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James J. Palestro,Per B. Sederberg,Adam F. Osth,Trisha Van Zandt,Brandon M. Turnerhe solution was based on a network of sensors and an electronic circuit board capable of acquiring, processing, and wirelessly sending data associated with the environmental variables to a web service developed for that purpose. The architecture’s implementation was designed to have five sensors rec
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James J. Palestro,Per B. Sederberg,Adam F. Osth,Trisha Van Zandt,Brandon M. Turnereural system models that can be isolated between different types of heart tumors. To perform this task, deep learning is used. It is a type of instrument-based learning where the lower levels responsible for many types of higher-level definitions appear above the different levels of the screen. This
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