000			01763 a2200301 4500
001			0367449005
005			20250317100417.0
008			250312042020xx eng
020			_a9780367449001
037			_bTaylor & Francis _cGBP 61.99 _fBB
040			_a01
041			_aeng
072	7		_aPBT _2thema
072	7		_aPS _2thema
072	7		_aT _2thema
072	7		_aPBT _2bic
072	7		_aPS _2bic
072	7		_aT _2bic
072	7		_aMAT029010 _2bisac
072	7		_aMAT029000 _2bisac
072	7		_a519.2 _2bisac
100	1		_aPeter Guttorp
245	1	0	_aStochastic Modeling of Scientific Data
250			_a1
260			_bChapman and Hall/CRC _c20201218
300			_a384 p
520			_bStochastic Modeling of Scientific Data combines stochastic modeling and statistical inference in a variety of standard and less common models, such as point processes, Markov random fields and hidden Markov models in a clear, thoughtful and succinct manner. The distinguishing feature of this work is that, in addition to probability theory, it contains statistical aspects of model fitting and a variety of data sets that are either analyzed in the text or used as exercises. Markov chain Monte Carlo methods are introduced for evaluating likelihoods in complicated models and the forward backward algorithm for analyzing hidden Markov models is presented. The strength of this text lies in the use of informal language that makes the topic more accessible to non-mathematicians. The combinations of hard science topics with stochastic processes and their statistical inference puts it in a new category of probability textbooks. The numerous examples and exercises are drawn from astronomy, geology, genetics, hydrology, neurophysiology and physics.
999			_c3009 _d3009