Introduction to bayesian statistics
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| William M. Bolstad | ||||
| Wiley-Interscience | ||||
| 2004 | ||||
| English | ||||
| 362 pages | ||||
| 3.62 MB | ||||
[tab] [content title="Description"]There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. In Bayesian statistics the rules of probability are used to make inferences about the parameter. Prior information about the parameter and sample information from the data are combined using Bayes theorem. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. This book uniquely covers the topics usually found in a typical introductory statistics book but from a Bayesian perspective. [/content] [content title="Content"] [/content] [content title="About the author"]William M. Bolstad, PhD, is a retired Senior Lecturer in the Department of Statistics at The University of Waikato, New Zealand. Dr. Bolstad's research interests include Bayesian statistics, MCMC methods, recursive estimation techniques, multiprocess dynamic time series models, and forecasting. He is author of Understanding Computational Bayesian Statistics, also published by Wiley. [/content] [/tab]
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