000			04105cam a22003734a 4500
999			_c1187 _d1187
001			16959764
003			OSt
005			20230621113100.0
008			110913s2012 flua b 001 0 eng
010			_a 2011033774
020			_a9781439839546 (hardback)
020			_a1439839549 (hardback)
035			_a(OCoLC)ocn752909681
040			_aDLC _cDLC _dYDX _dYDXCP _dBWX _dCDX _dDLC
042			_apcc
050	0	0	_aQA279.5 _b.W655 2012
082	0	0	_a519.5/42028553 _223
084			_aMAT029000 _2bisacsh
100	1		_aWoodward, Phillip.
245	1	0	_aBayesian analysis made simple : _ban Excel GUI for WinBUGS / _cPhil Woodward.
260			_aBoca Raton, FL : _bCRC Press, _cc2012.
300			_axx, 342 p. : _bill. ; _c25 cm.
490	1		_aChapman & Hall/CRC biostatistics series
504			_aIncludes bibliographical references and index.
520			_a"Although the popularity of the Bayesian approach to statistics has been growing for years, many still think of it as somewhat esoteric, not focused on practical issues, or generally too difficult to understand.Bayesian Analysis Made Simple is aimed at those who wish to apply Bayesian methods but either are not experts or do not have the time to create WinBUGS code and ancillary files for every analysis they undertake. Accessible to even those who would not routinely use Excel, this book provides a custom-made Excel GUI, immediately useful to those users who want to be able to quickly apply Bayesian methods without being distracted by computing or mathematical issues.From simple NLMs to complex GLMMs and beyond, Bayesian Analysis Made Simple describes how to use Excel for a vast range of Bayesian models in an intuitive manner accessible to the statistically savvy user. Packed with relevant case studies, this book is for any data analyst wishing to apply Bayesian methods to analyze their data, from professional statisticians to statistically aware scientists"-- _cProvided by publisher.
520			_a"Preface Although the popularity of the Bayesian approach to statistics has been growing rapidly for many years, among those working in business and industry there are still many who think of it as somewhat esoteric, not focused on practical issues, or generally quite difficult to understand. This view may be partly due to the relatively few books that focus primarily on how to apply Bayesian methods to a wide range of common problems. I believe that the essence of the approach is not only much more relevant to the scientific problems that require statistical thinking and methods, but also much easier to understand and explain to the wider scientific community. But being convinced of the benefits of the Bayesian approach is not enough if the person charged with analyzing the data does not have the computing software tools to implement these methods. Although WinBUGS (Lunn et al. 2000) provides sufficient functionality for the vast majority of data analyses that are undertaken, there is still a steep learning curve associated with the programming language that many will not have the time or motivation to overcome. This book describes a graphical user interface (GUI) for WinBUGS, BugsXLA, the purpose of which is to make Bayesian analysis relatively simple. Since I have always been an advocate of Excel as a tool for exploratory graphical analysis of data (somewhat against the anti-Excel feelings in the statistical community generally), I created BugsXLA as an Excel add-in. Other than to calculate some simple summary statistics from the data, Excel is only used as a convenient vehicle to store the data, plus some meta-data used by BugsXLA, as well as a home for the Visual Basic program itself"-- _cProvided by publisher.
630	0	0	_aMicrosoft Excel (Computer file)
630	0	0	_aWinBUGS.
650		0	_aBayesian statistical decision theory.
830		0	_aChapman & Hall/CRC biostatistics series.
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2lcc _cGENERAL