Robust adaptive Metropolis algorithm with coerced acceptance rate

Details Robust adaptive Metropolis algorithm with coerced acceptance rate Robust adaptive Metropolis algorithm with coerced acceptance rate

2010-11-19

arxiv.org/abs/1011.4381v2

Statistics

Computation

21 pages, 3 figures

Scientific paper

The adaptive Metropolis (AM) algorithm of Haario, Saksman and Tamminen [Bernoulli 7 (2001) 223-242] uses the estimated covariance of the target distribution in the proposal distribution. This paper introduces a new robust adaptive Metropolis algorithm estimating the shape of the target distribution and simultaneously coercing the acceptance rate. The adaptation rule is computationally simple adding no extra cost compared with the AM algorithm. The adaptation strategy can be seen as a multidimensional extension of the previously proposed method adapting the scale of the proposal distribution in order to attain a given acceptance rate. The empirical results show promising behaviour of the new algorithm in an example with Student target distribution having no finite second moment, where the AM covariance estimate is unstable. In the examples with finite second moments, the performance of the new approach seems to be competitive with the AM algorithm combined with scale adaptation.

Affiliated with

Also associated with

No associations

Rating

Robust adaptive Metropolis algorithm with coerced acceptance rate does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Robust adaptive Metropolis algorithm with coerced acceptance rate, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Robust adaptive Metropolis algorithm with coerced acceptance rate will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-117049