{"product_id":"9780387948195","title":"Smoothness Priors Analysis of Time Series","description":"Smoothness Priors Analysis of Time Series 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.","brand":"Genshiro Kitagawa, Will Gersch","offers":[{"title":"Default Title","offer_id":45770278273342,"sku":"9780387948195","price":3842.87,"currency_code":"ZAR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4357\/7662\/products\/9780387948195_22d45112-ff93-4344-930c-9ae6cef9d048.jpg?v=1707250591","url":"https:\/\/exclusivebooks.co.za\/products\/9780387948195","provider":"Exclusive Books Online","version":"1.0","type":"link"}