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| #1571668 in Books | Barber David EDT | 2011-09-30 | Original language:English | PDF # 1 | 9.72 x.94 x6.85l,1.85 | File type: PDF | 432 pages | Bayesian Time Series Models||22 of 23 people found the following review helpful.| Advanced topics|By Dimitri Shvorob|I am not qualified to judge quality and scope of this book's material - I am inclined to give very high marks on both, though - but will hopefully add value by (a) pointing out that this is in fact a collection of advanced papers, not a textbook, (b) suggesting two more-beginner-friendly books on the subject, "Dynamic linear models with R"||"this book is well organized. The experts in this field provide both breadth and depth. The book is suitable for statisticians, engineers, and computer scientists. Readers can definitely learn state-of-the-art techniques from it." Hsun-Hsien Chang, Comp
'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical models, the book covers approximation schemes, both Monte Carlo and deterministic, and introduces switching, multi-object, non-parametric and agent...
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