000			02633nam a22003498i 4500
001			CR9781108123891
003			UkCbUP
005			20200124160336.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			160812s2017||||enk o ||1 0|eng|d
020			_a9781108123891 (ebook)
020			_z9781107192119 (hardback)
020			_z9781316642214 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQC20.7.B38 _bB35 2017
082	0	0	_a519.5/42 _223
100	1		_aBailer-Jones, Coryn A. L., _eauthor.
245	1	0	_aPractical Bayesian inference : _ba primer for physical scientists / _cCoryn A.L. Bailer-Jones.
264		1	_aCambridge : _bCambridge University Press, _c2017.
300			_a1 online resource (ix, 295 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 17 Jul 2017).
505	0		_aProbability basics -- Estimation and uncertainty -- Statistical models and inference -- Linear models, least squares, and maximum likelihood -- Parameter estimation: single parameter -- Parameter estimation: multiple parameters -- Approximating distributions -- Monte Carlo methods for inference -- Parameter estimation: Markov Chain Monte Carlo -- Frequentist hypothesis testing -- Model comparison -- Dealing with more complicated problems.
520			_aScience is fundamentally about learning from data, and doing so in the presence of uncertainty. This volume is an introduction to the major concepts of probability and statistics, and the computational tools for analysing and interpreting data. It describes the Bayesian approach, and explains how this can be used to fit and compare models in a range of problems. Topics covered include regression, parameter estimation, model assessment, and Monte Carlo methods, as well as widely used classical methods such as regularization and hypothesis testing. The emphasis throughout is on the principles, the unifying probabilistic approach, and showing how the methods can be implemented in practice. R code (with explanations) is included and is available online, so readers can reproduce the plots and results for themselves. Aimed primarily at undergraduate and graduate students, these techniques can be applied to a wide range of data analysis problems beyond the scope of this work.
650		0	_aBayesian statistical decision theory.
650		0	_aMathematical physics.
776	0	8	_iPrint version: _z9781107192119
856	4	0	_uhttps://doi.org/10.1017/9781108123891
999			_c523076 _d523074