Optimal Scaling of the Random Walk Metropolis: General Criteria for the 0.234 Acceptance Rule
2013 ◽
Vol 50
(1)
◽
pp. 1-15
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Keyword(s):
Scaling of proposals for Metropolis algorithms is an important practical problem in Markov chain Monte Carlo implementation. Analyses of the random walk Metropolis for high-dimensional targets with specific functional forms have shown that in many cases the optimal scaling is achieved when the acceptance rate is approximately 0.234, but that there are exceptions. We present a general set of sufficient conditions which are invariant to orthonormal transformation of the coordinate axes and which ensure that the limiting optimal acceptance rate is 0.234. The criteria are shown to hold for the joint distribution of successive elements of a stationary pth-order multivariate Markov process.
2013 ◽
Vol 50
(01)
◽
pp. 1-15
◽
2017 ◽
Vol 54
(4)
◽
pp. 1233-1260
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2010 ◽
Vol 13
(3)
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pp. 583-601
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2015 ◽
Vol 18
(3)
◽
pp. 869-884
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2003 ◽
Vol 40
(1)
◽
pp. 123-146
◽
2003 ◽
Vol 40
(01)
◽
pp. 123-146
◽
2015 ◽
Vol 25
(4)
◽
pp. 2263-2300
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2007 ◽
Vol 10
(2)
◽
pp. 277-297
◽
2020 ◽
Vol 130
(10)
◽
pp. 6094-6132