+- Softwarez.Info - Software's World! (https://softwarez.info)
+-- Forum: Library Zone (https://softwarez.info/Forum-Library-Zone)
+--- Forum: E-Books (https://softwarez.info/Forum-E-Books)
+--- Thread: Smoothness Priors Analysis of Time Series (/Thread-Smoothness-Priors-Analysis-of-Time-Series)
Smoothness Priors Analysis of Time Series - ebooks1001 - 11-28-2024
![[Image: e9070b7c805286cdc195517f99705720.webp]](https://i124.fastpic.org/big/2024/1128/20/e9070b7c805286cdc195517f99705720.webp)
Free Download Smoothness Priors Analysis of Time Series By Genshiro Kitagawa, Will Gersch (auth.)
1996 | 280 Pages | ISBN: 0387948198 | PDF | 9 MB
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.
Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live
Links are Interchangeable - Single Extraction