Optimal scaling for the pseudo-marginal random walk Metropolis:insensitivity to the noise generating mechanism

Sherlock, Christopher (2016) Optimal scaling for the pseudo-marginal random walk Metropolis:insensitivity to the noise generating mechanism. Methodology and Computing in Applied Probability, 18 (3). pp. 869-884. ISSN 1387-5841

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Abstract

We examine the optimal scaling and the efficiency of the pseudo-marginal random walk Metropolis algorithm using a recently-derived result on the limiting efficiency as the dimension, d→∞. We prove that the optimal scaling for a given target varies by less than 20 % across a wide range of distributions for the noise in the estimate of the target, and that any scaling that is within 20 % of the optimal one will be at least 70 % efficient. We demonstrate that this phenomenon occurs even outside the range of noise distributions for which we rigorously prove it. We then conduct a simulation study on an example with d = 10 where importance sampling is used to estimate the target density; we also examine results available from an existing simulation study with d = 5 and where a particle filter was used. Our key conclusions are found to hold in these examples also.

Item Type:
Journal Article
Journal or Publication Title:
Methodology and Computing in Applied Probability
Additional Information:
The final publication is available at Springer via http://dx.doi.org/10.1007/s11009-015-9471-6
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600
Subjects:
ID Code:
76751
Deposited By:
Deposited On:
20 Nov 2015 16:36
Refereed?:
Yes
Published?:
Published
Last Modified:
12 Jul 2020 05:21