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The Simulation of Bayesian Dynamic Models

Author: SuBing
Tutor: JiangXiaoZuo
School: Shandong University
Course: Applied Mathematics
Keywords: Bayesian dynamic model Stochastic simulation Bayes factor Model monitoring
CLC: O212.8
Type: Master's thesis
Year: 2007
Downloads: 94
Quote: 1
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Abstract


This paper studies non-the normality assumption dynamic linear model observation equation: y t = F t T θ t ν t , ν t ~ [0, V t ]; equation of state: θ t = G t θ t-1 ω t , ω t ~ [0, W t ]. (\t ) ~ p (y t | θ t ); state equation: (y t | θ t- 1 ) to P (y t | θ T-1 ) (\function) simulation algorithm, and given a new Bayesian model, and finally discuss the Bayesian model selection and monitoring, which reads as follows: Bayesian theory of the development of the process outlined in the first chapter, introduced Tony dynamic model theory Julius basic concepts and problems encountered in the current research in this area, and the framework of the entire paper with a brief summary. stochastic simulation methods introduced in the second chapter, the main importance of sampling Metropolis-Hastings sampling, Gibbs sampling, three related to this article. latter two methods occupies an important position in the dynamic model simulations, we have carried out a more detailed study, including sample extraction, sample convergence diagnostic specifically given a diagnosis method: Riemann and convergence diagnostics in the third chapter, stochastic simulation methods to deal with these two models above the main idea: instead of the density function of the sample correction recursive recursive correction, and then the samples for a variety of inference; different recursive algorithm for a different model, in this process, in this chapter, a number of convex density new dynamic model, and prove that it meets the \Luo ways to strike a Bayes factor, then through Bayes factor model selection. the Bayes factor mentioned in the literature [2] in the complex case is no longer applicable, proposed a new Bayes factor, it will be discussing the model is extremely useful given two model monitoring methods in Chapter One is the use of the observation error constructed a χ 2 - Statistics amount, using the statistics to the accuracy of the set is given a truncation point with the truncation point on the model to achieve the local and overall monitoring; second is to propose a new Bayesian factor, which is adjacent than the moment observations marginal density, can achieve a partial monitoring of the model this moment.

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CLC: > Mathematical sciences and chemical > Mathematics > Probability Theory and Mathematical Statistics > Mathematical Statistics > Bayesian statistics
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