New PDF release: Bayesian Time Series Models

By Barber D., Cemgil A.T., Chiappa S. (eds.)

ISBN-10: 0521196760

ISBN-13: 9780521196765

'What's going to take place next?' Time sequence info carry the solutions, and Bayesian equipment signify the innovative in studying what they must say. This formidable booklet is the 1st unified remedy of the rising knowledge-base in Bayesian time sequence options. Exploiting the unifying framework of probabilistic graphical versions, the ebook covers approximation schemes, either Monte Carlo and deterministic, and introduces switching, multi-object, non-parametric and agent-based versions in quite a few program environments. It demonstrates that the elemental framework helps the speedy production of types adapted to precise functions and provides perception into the computational complexity in their implementation. The authors span conventional disciplines equivalent to facts and engineering and the extra lately confirmed parts of desktop studying and development attractiveness. Readers with a uncomplicated figuring out of utilized likelihood, yet no adventure with time sequence research, are guided from primary innovations to the cutting-edge in learn and perform

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Additional resources for Bayesian Time Series Models

Example text

E. the sequence {θk , k ≥ 0} may move at an infinite distance from the initial value θ0 . It is not necessarily required that the parameters {θk , k ≥ 0} converge to some fixed value. An in-depth description of controlled MCMC algorithms is given in [5], illustrated with many examples (some of which are given below). 3) converge to the set of solutions of the equation h(θ) = 0 where θ → h(θ) is the mean-field associated to H defined as def h(θ) = H(θ, x, x )π(dx)Pθ (x, dx ). X The convergence of Eq.

11 A typical realisation from the changepoint model. The time index is indicated by t and the number of counts by yt . 2. Using this proposal results in a MH acceptance probability of 1, so that every candidate sample is accepted. Dealing with evidence (variables in known states) is straightforward – one sets the evidential variables into their states and samples from the remaining variables. Example: Gibbs sampling for a changepoint model We illustrate the Gibbs sampler on a changepoint model for count data [13].

Capp´e, E. Moulines and T. Ryd´en. Inference in Hidden Markov Models. Springer-Verlag, 2005. [18] N. Metropolis and S. Ulam. The Monte Carlo method. Journal of the American Statistical Association, 44(247):335–341, 1949. [5] A. Dempster, N. Laird and D. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39(1):1–38, 1977. [19] T. Minka. Expectation Propagation for approximate Bayesian inference. PhD thesis, MIT, 2001. [6] A. Doucet, N.

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Bayesian Time Series Models by Barber D., Cemgil A.T., Chiappa S. (eds.)


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