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Study on Forecasting and Simulation of the Nonlinear Bayesian Dynamic Models

Author: LiJinLong
Tutor: LiuFuSheng
School: Shandong University of Science and Technology
Course: Applied Mathematics
Keywords: The nonlinear Bayesian dynamic model Sequential importance sampling Sequence imputation algorithm MCMC method The nuclear M.West smooth density Gonden et al method Bayes factor
CLC: O212
Type: Master's thesis
Year: 2003
Downloads: 397
Quote: 4
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Abstract


This paper discusses the application of stochastic simulation to study the nonlinear Bayesian dynamic model . On the nonlinear Bayesian dynamic model of the two major forms of ( a ) the general model ( 2 ) with a parameter model proposed several targeted simulation method . For example, for the following general model : application of the sequential importance sampling method and MCMC method Gibbs sampling algorithm for prediction of the model , and for the selection and sampling of the important functions of the feasibility of proposed several conclusions . Follows the smooth nuclear density theory and sequence parameters model utilization M.West of the imputation algorithm , the model predicts the problem has been resolved . And in the discussion of the loss of information when the sampling process model parameters and found that the intrinsic link between M.West and Gonden et al method . In addition , for the MCMC algorithm Metropolis algorithm . Also to some extent explored for the possibility of non-linear dynamic Bayesian model .

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