Metropolis-Hastings Algorithm with Delayed Acceptance and Rejection
Author(s) -
Yulin Hu,
Ya-Yong Tang
Publication year - 2019
Publication title -
review of educational theory
Language(s) - English
Resource type - Journals
eISSN - 2591-7633
pISSN - 2591-7625
DOI - 10.30564/ret.v2i2.682
Subject(s) - metropolis–hastings algorithm , algorithm , computer science , computation , mathematics , artificial intelligence , markov chain monte carlo , bayesian probability
Article history Received: 25 March 2019 Revised: 1 April 2019 Accepted: 23 April 2019 Published Online: 30 April 2019 Metropolis-Hastings algorithms are slowed down by the computation of complex target distributions. To solve this problem, one can use the delayed acceptance Metropolis-Hastings algorithm (MHDA) of Christen and Fox (2005). However, the acceptance rate of a proposed value will always be less than in the standard Metropolis-Hastings. We can x this problem by using the Metropolis-Hastings algorithm with delayed rejection (MHDR) proposed by Tierney and Mira (1999). In this paper, we combine the ideas of MHDA and MHDR to propose a new MH algorithm, named the Metropolis-Hastings algorithm with delayed acceptance and rejection (MHDAR). The new algorithm reduces the computational cost by division of the prior or likelihood functions and increase the acceptance probability by delay rejection of the second stage. We illustrate those accelerating features by a realistic example.
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