000			02008nam a22002177a 4500
005			20240328171644.0
008			240224b |||||||| |||| 00| 0 eng d
020			_a9780198841302
082			_a519.542 _bDON
100			_aDonovan, Therese M. _914399
245			_aBayesian statistics for beginners: _ba step-by-step approach
260			_bOxford University Press _aOxford _c2019
300			_ax, 419 p.
365			_aGBP _b52.00
520			_aBayesian Statistics for Beginners is an entry-level book on Bayesian statistics. It is like no other math book you’ve read. It is written for readers who do not have advanced degrees in mathematics and who may struggle with mathematical notation, yet need to understand the basics of Bayesian inference for scientific investigations. Intended as a “quick read,” the entire book is written as an informal, humorous conversation between the reader and writer—a natural way to present material for those new to Bayesian inference. The most impressive feature of the book is the sheer length of the journey, from introductory probability to Bayesian inference and applications, including Markov Chain Monte Carlo approaches for parameter estimation, Bayesian belief networks, and decision trees. Detailed examples in each chapter contribute a great deal, where Bayes’ Theorem is at the front and center with transparent, step-by-step calculations. A vast amount of material is covered in a lighthearted manner; the journey is relatively pain-free. The book is intended to jump-start a reader’s understanding of probability, inference, and statistical vocabulary that will set the stage for continued learning. Other features include multiple links to web-based material, an annotated bibliography, and detailed, step-by-step appendices. (https://academic.oup.com/book/41940)
650			_aStatistics _915518
650			_aProbability _916585
650			_a Biomathematics and Statistics _916586
700			_aMickey, Ruth M. _916387
942			_cBK _2ddc
999			_c6146 _d6146