000 02439nam a2200349 4500
001 OTLid0000288
003 MnU
005 20201105133313.0
006 m o d s
008 180907s2012 mnu o 0 0 eng d
020 _a9781449370787
040 _aMnU
_beng
_cMnU
050 4 _aQA76
100 1 _aDowney, Allen B.
_eauthor
245 0 0 _aThink Bayes
_bBayesian Statistics Made Simple
_cAllen Downey
264 2 _bOpen Textbook Library
264 1 _bGreen Tea Press
300 _a1 online resource
490 0 _aOpen textbook library.
505 0 _aPreface -- 1 Bayes's Theorem -- 2 Computational Statistics -- 3 Estimation -- 4 More Estimation -- 5 Odds and Addends -- 6 Decision Analysis -- 7 Prediction -- 8 Observer Bias -- 9 Two Dimensions -- 10 Approximate Bayesian Computation -- 11 Hypothesis Testing -- 12 Evidence -- 13 Simulation -- 14 A Hierarchical Model -- 15 Dealing with Dimensions
520 0 _aThink Bayes is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics. Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops. I think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems.
542 1 _fAttribution-NonCommercial
546 _aIn English.
588 0 _aDescription based on online resource
650 0 _aComputer Science
_vTextbooks
710 2 _aOpen Textbook Library
_edistributor
856 4 0 _uhttps://open.umn.edu/opentextbooks/textbooks/288
_zAccess online version
999 _c19688
_d19688