{"product_id":"smoothness-priors-analysis-of-time-series-von-will-gersch-genshiro-kitagawa","title":"Smoothness Priors Analysis of Time Series","description":"\n                                \n                \u003cb\u003eSmoothness Priors Analysis of Time Series\u003c\/b\u003e\n                                 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.\n            \n            \u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9780387948195\"\u003e\u003ch3\u003e\u003c\/h3\u003e\u003c\/div\u003e","brand":"Libri","offers":[{"title":"Softcover - 9780387948195","offer_id":39415130882141,"sku":"9780387948195","price":139.09,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/e3ab533f-ebbe-4677-8ad0-f6ce7f1c1f00.jpg?v=1782446623","url":"https:\/\/shop.autorenwelt.de\/en\/products\/smoothness-priors-analysis-of-time-series-von-will-gersch-genshiro-kitagawa","provider":"Autorenwelt Shop","version":"1.0","type":"link"}