{"product_id":"bayesian-analysis-of-time-series-9780367779993","title":"Bayesian Analysis of Time Series","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIn many branches of science relevant observations are taken sequentially over time. \u003ci\u003eBayesian Analysis of Time Series\u003c\/i\u003e discusses how to use models that explain the probabilistic characteristics of these time series and then utilizes the Bayesian approach to make inferences about their parameters. This is done by taking the prior information and via Bayes theorem implementing Bayesian inferences of estimation, testing hypotheses, and prediction. The methods are demonstrated using both R and WinBUGS. The R package is primarily used to generate observations from a given time series model, while the WinBUGS packages allows one to perform a posterior analysis that provides a way to determine the characteristic of the posterior distribution of the unknown parameters.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eFeatures\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e \u003cbr\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e \u003cli\u003ePresents a comprehensive introduction to the Bayesian analysis of time series. \u003c\/li\u003e \u003cbr\u003e\u003cbr\u003e \u003cbr\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e \u003cli\u003eGives many examples over a wide variety of fields\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\"...(This book) by Lyle D. Broemeling is an excellent source to learn time series concepts, methods, expressions, and interpretations from the Bayesian viewpoint using R code and WinBugs code...The book is suitable for usage to teach in a graduate-level Bayesian time series course...The references are exhaustive and well selected for the readers. The exercises are challenging.\"\u003cbr\u003e- Ramalingam Shanmugam, JSCS, Aug 2020\u003c\/p\u003e\n\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e1. Introduction. 2. Bayesian Inference : The prior, posterior and predictive distributions. 3. Plot Trends , Seasonal Variation and Decomposition of a Series. 4. Autocorrelation, Partial Correlation, and Cross Correlation. 5. Bayesian Data Analysis for Some Fundamental Time Series. 6. Bayesian Regression Analysis with Time Series Errors. 7. Bayesian Methods for Stationary Models 8. An Analysis for Non-Stationary Models. 9. Bayesian Spectrum Analysis. 10. System Identification from a Bayesian Perspective. 11. Multivariate Models. 12. Dynamic Linear Models for Time Series. 13. Bayesian Posterior Distributions for Non-Linear Models.14. Bilinear Models and Threshold Autoregressive Processes. 15. Miscellaneous Topics in Time Series.\u003c\/p\u003e\n\u003cp\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"CRC Press","offers":[{"title":"Default Title","offer_id":52090682835287,"sku":"9780367779993","price":45.59,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780367779993.jpg?v=1762273068","url":"https:\/\/bookcurl.com\/products\/bayesian-analysis-of-time-series-9780367779993","provider":"Book Curl","version":"1.0","type":"link"}