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040			_aNISER LIBRARY _beng _cNISER LIBRARY
082			_a519.226 _bBRO-B
100			_aBroemeling, Lyle D.
245			_aBayesian inference for stochastic processes
260			_aBoca Raton, FL : _bCRC Press, Taylor & Francis Group, _c2018.
300			_axv, 432 pages : _billustrations ; _c26 cm
504			_aIncludes bibliographical references and index
520			_aThis is the first book designed to introduce Bayesian inference procedures for stochastic processes. There are clear advantages to the Bayesian approach (including the optimal use of prior information). Initially, the book begins with a brief review of Bayesian inference and uses many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete state space and continuous time and continuous state space. The elements necessary to understanding stochastic processes are then introduced, followed by chapters devoted to the Bayesian analysis of such processes. It is important that a chapter devoted to the fundamental concepts in stochastic processes is included. Bayesian inference (estimation, testing hypotheses, and prediction) for discrete time Markov chains, for Markov jump processes, for normal processes (e.g. Brownian motion and the Ornstein–Uhlenbeck process), for traditional time series, and, lastly, for point and spatial processes are described in detail. Heavy emphasis is placed on many examples taken from biology and other scientific disciplines. In order analyses of stochastic processes, it will use R and WinBUGS.
650			_aBayesian statistical decision theory
650			_aProbabilities
650			_aStochastic processes
856			_3Table of content _uhttps://www.routledge.com/Bayesian-Inference-for-Stochastic-Processes/Broemeling/p/book/9780367572433#googlePreviewContainer
856			_3Reviews _uhttps://www.goodreads.com/book/show/53907450-bayesian-inference-for-stochastic-processes?ref=nav_sb_ss_1_13#CommunityReviews
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