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Approximate Variational Message Propagation for GMRF

Author: YinXia
Tutor: LiaoShiZhong
School: Tianjin University
Course: Applied Computer Technology
Keywords: Gaussian Markov random fields Variational inference Gaussian split ring tree Approximate variational message spread
CLC: TP18
Type: Master's thesis
Year: 2010
Downloads: 24
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Abstract


The variational inference Application variational iteration calculate the approximate expected value of the random variable is a modern artificial intelligence is an important approximation probabilistic reasoning . With the increase in the number of variational iteration , the variational iteration process involves more and more variable , but the new variable impact on the accuracy of the smaller of the recent developments in incomplete iterative approximate variational inference method . In this paper, Gaussian Markov Random Field Model incomplete iterative approximate variational inference method to calculate the exact lower bound of the variable expectations , the main work includes : 1. Definition of the concept of Gaussian Markov random field model Gaussian split ring tree , can be expressed in the form of Gaussian Markov random Field Model iterative calculation process . 2 proposed the correctness of the split ring tree based on Gaussian approximation variational message propagation algorithm , proved variable expect the lower bound theorem , to ensure that the method further analysis of the computational complexity of the approximate message propagation algorithm . Design numerical simulation and experimental verification of the correctness of the algorithm and comparative analysis of the different Gaussian Markov expectations with the variables in the model of the airport sector compactness and computational complexity .

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