Bayesian Cognitive Modeling by Michael D. Lee
Bayesian Cognitive Modeling by Michael D. Lee
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DOI：
10.1017/cbo9781139087759
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发表时间：
2014-04
期刊：
影响因子：
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通讯作者：
M. Lee;E. Wagenmakers
中科院分区：
文献类型：
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作者：
M. Lee;E. Wagenmakers
贝叶斯推理已成为许多科学领域的标准分析方法。然而，实验心理学和认知科学的学生和研究人员未能充分利用贝叶斯方法提供的新的和令人兴奋的可能性。理想的教学和自学，这本书演示了如何做贝叶斯建模。简短扼要的章节提供了示例、练习和计算机代码（使用WinBUGS或JAGS，并由Matlab和R支持），以及在线提供的其他支持。不需要统计学的高级知识，从一开始，就鼓励读者自己应用和调整贝叶斯分析。这本书包含了一系列关于参数估计和模型选择的章节，随后是来自认知科学的详细案例研究。读完本书后，读者应该能够构建自己的贝叶斯模型，将模型应用于自己的数据，并得出自己的结论。
Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.