| 书目名称 | Introduction to Applied Bayesian Statistics and Estimation for Social Scientists |
| 编辑 | Scott M. Lynch |
| 视频video | |
| 概述 | First book written at an introductory level for social scientists interested in learning about MCMC.Includes supplementary material: |
| 丛书名称 | Statistics for Social and Behavioral Sciences |
| 图书封面 |  |
| 描述 | ."Introduction to Applied Bayesian Statistics and Estimation for Social Scientists" covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail...The first part of the book provides a detailed introduction to mathematical statistics and the Bayesian approach to statistics, as well as a thorough explanation of the rationale for using simulation methods to construct summaries of posterior distributions. Markov chain Monte Carlo (MCMC) methods - including the Gibbs sampler and the Metropolis-Hastings algorithm - are then introduced as general methods for simulating samples from distributions. Extensive discussion of programming MCMC algorithms, monitoring their performance, and improving them is provided before turning to the larger examples involving real social science models and d |
| 出版日期 | Book 2007 |
| 关键词 | Generalized linear model; Probability theory; Statistical Inference; bayesian statistics; best fit; linea |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-0-387-71265-9 |
| isbn_softcover | 978-1-4419-2434-6 |
| isbn_ebook | 978-0-387-71265-9Series ISSN 2199-7357 Series E-ISSN 2199-7365 |
| issn_series | 2199-7357 |
| copyright | Springer-Verlag New York 2007 |
发表于 2025-3-21 19:39:38
