Author: Liu, Wendi; Tang, Sanyi; Xiao, Yanni
Title: Model Selection and Evaluation Based on Emerging Infectious Disease Data Sets including A/H1N1 and Ebola Document date: 2015_9_15
ID: 0j4is0n4_12
Snippet: Model Selection. The principle of MCMC methods can be briefly described as follows: build a transition kernel with stationary distribution ( ) (which is a target density) and then generate a Markov chain ( ) using this kernel such that the limiting distribution of ( ) is ( ). The integral ∫ ℎ( ) ( ) can be approximated with the standard average (1/ ) ∑ 0 ℎ( ( ) ). The Metropolis-Hastings (MH) algorithm is one of the methods to realize MCM.....
Document: Model Selection. The principle of MCMC methods can be briefly described as follows: build a transition kernel with stationary distribution ( ) (which is a target density) and then generate a Markov chain ( ) using this kernel such that the limiting distribution of ( ) is ( ). The integral ∫ ℎ( ) ( ) can be approximated with the standard average (1/ ) ∑ 0 ℎ( ( ) ). The Metropolis-Hastings (MH) algorithm is one of the methods to realize MCMC algorithm, which can produce a Markov chain ( ) with the objective density ( ) and the transition probability (⋅, ⋅). The algorithm is as follows.
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