The book "Bayesian Cognitive Modeling: A Practical Course" by Michael D. Lee and Eric-Jan Wagenmakers is a comprehensive guide to Bayesian modeling, particularly using the WinBUGS software. The book is designed to be accessible to readers with only basic computer skills, no advanced knowledge of statistics or mathematics, and a focus on practical application in cognitive science. It covers the fundamentals of Bayesian analysis, including principles, parameter estimation, and data analysis, with a strong emphasis on hands-on practice using WinBUGS. The content is divided into three main parts: Getting Started, Parameter Estimation, and Answers. The first part introduces Bayesian basics, such as general principles, prediction, sequential updating, and Markov Chain Monte Carlo (MCMC) methods. The second part delves into parameter estimation using binomial and Gaussian distributions, providing detailed examples and exercises. The third part offers solutions to the exercises, reinforcing the concepts covered. The authors emphasize the flexibility and practicality of Bayesian modeling, highlighting its suitability for complex cognitive science problems. They also provide guidance on installing and using WinBUGS, Matlab, and R, and offer a range of references for further reading. The book aims to equip readers with the skills to build and apply Bayesian models to their own data, making it a valuable resource for researchers and students in cognitive science.The book "Bayesian Cognitive Modeling: A Practical Course" by Michael D. Lee and Eric-Jan Wagenmakers is a comprehensive guide to Bayesian modeling, particularly using the WinBUGS software. The book is designed to be accessible to readers with only basic computer skills, no advanced knowledge of statistics or mathematics, and a focus on practical application in cognitive science. It covers the fundamentals of Bayesian analysis, including principles, parameter estimation, and data analysis, with a strong emphasis on hands-on practice using WinBUGS. The content is divided into three main parts: Getting Started, Parameter Estimation, and Answers. The first part introduces Bayesian basics, such as general principles, prediction, sequential updating, and Markov Chain Monte Carlo (MCMC) methods. The second part delves into parameter estimation using binomial and Gaussian distributions, providing detailed examples and exercises. The third part offers solutions to the exercises, reinforcing the concepts covered. The authors emphasize the flexibility and practicality of Bayesian modeling, highlighting its suitability for complex cognitive science problems. They also provide guidance on installing and using WinBUGS, Matlab, and R, and offer a range of references for further reading. The book aims to equip readers with the skills to build and apply Bayesian models to their own data, making it a valuable resource for researchers and students in cognitive science.