{"product_id":"smoothness-priors-analysis-of-time-series-9780387948195","title":"Smoothness Priors Analysis of Time Series","description":"\u003cp\u003e • Author(s): Genshiro Kitagawa\u003cbr\u003e • Publisher: Springer\u003cbr\u003e • Publisher Imprint: Springer\u003cbr\u003e • BISAC: General\u003c\/p\u003e\u003cp\u003e\u003cb\u003eSmoothness Priors Analysis of Time Series\u003c\/b\u003e addresses some of the problems of modeling stationary and nonstationary time series primarily from a Bayesian stochastic regression \"smoothness priors\" state space point of view. Prior distributions on model coefficients are parametrized by hyperparameters. Maximizing the likelihood of a small number of hyperparameters permits the robust modeling of a time series with relatively complex structure and a very large number of implicitly inferred parameters. The critical statistical ideas in smoothness priors are the likelihood of the Bayesian model and the use of likelihood as a measure of the goodness of fit of the model. The emphasis is on a general state space approach in which the recursive conditional distributions for prediction, filtering, and smoothing are realized using a variety of nonstandard methods including numerical integration, a Gaussian mixture distribution-two filter smoothing formula, and a Monte Carlo \"particle-path tracing\" method in which the distributions are approximated by many realizations. The methods are applicable for modeling time series with complex structures.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Paperback","offer_id":45282054537367,"sku":"9780387948195","price":9548.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9780387948195.webp?v=1769301010","url":"https:\/\/atlanticbooks.com\/products\/smoothness-priors-analysis-of-time-series-9780387948195","provider":"Atlantic Books","version":"1.0","type":"link"}