Adaptive proposal distribution for random walk Metropolis algorithm

1999 ◽  
Vol 14 (3) ◽  
pp. 375-395 ◽  
Author(s):  
Heikki Haario ◽  
Eero Saksman ◽  
Johanna Tamminen
Keyword(s):  
Random Walk ◽  
Metropolis Algorithm ◽  
Proposal Distribution ◽  
Random Walk Metropolis ◽  
Random Walk Metropolis Algorithm

scholarly journals Asymptotic analysis of the random walk Metropolis algorithm on ridged densities

2018 ◽  
Vol 28 (5) ◽  
pp. 2966-3001 ◽  
Author(s):  
Alexandros Beskos ◽  
Gareth Roberts ◽  
Alexandre Thiery ◽  
Natesh Pillai
Keyword(s):  
Random Walk ◽  
Asymptotic Analysis ◽  
Metropolis Algorithm ◽  
Random Walk Metropolis ◽  
Random Walk Metropolis Algorithm

On the geometric ergodicity of hybrid samplers

2003 ◽  
Vol 40 (1) ◽  
pp. 123-146 ◽  
Author(s):  
G. Fort ◽  
E. Moulines ◽  
G. O. Roberts ◽  
J. S. Rosenthal
Keyword(s):  
Random Walk ◽  
Sufficient Conditions ◽  
Metropolis Algorithm ◽  
Geometric Ergodicity ◽  
Target Density ◽  
Symmetric Random Walk ◽  
Uniform Ergodicity ◽  
Random Walk Metropolis ◽  
Random Walk Metropolis Algorithm

In this paper, we consider the random-scan symmetric random walk Metropolis algorithm (RSM) on ℝd. This algorithm performs a Metropolis step on just one coordinate at a time (as opposed to the full-dimensional symmetric random walk Metropolis algorithm, which proposes a transition on all coordinates at once). We present various sufficient conditions implying V-uniform ergodicity of the RSM when the target density decreases either subexponentially or exponentially in the tails.

On the geometric ergodicity of hybrid samplers

2003 ◽  
Vol 40 (01) ◽  
pp. 123-146 ◽  
Author(s):  
G. Fort ◽  
E. Moulines ◽  
G. O. Roberts ◽  
J. S. Rosenthal
Keyword(s):  
Random Walk ◽  
Sufficient Conditions ◽  
Metropolis Algorithm ◽  
Geometric Ergodicity ◽  
Target Density ◽  
Symmetric Random Walk ◽  
Uniform Ergodicity ◽  
Random Walk Metropolis ◽  
Random Walk Metropolis Algorithm

In this paper, we consider the random-scan symmetric random walk Metropolis algorithm (RSM) on ℝ d . This algorithm performs a Metropolis step on just one coordinate at a time (as opposed to the full-dimensional symmetric random walk Metropolis algorithm, which proposes a transition on all coordinates at once). We present various sufficient conditions implying V-uniform ergodicity of the RSM when the target density decreases either subexponentially or exponentially in the tails.

scholarly journals Hierarchical Models and Tuning of Random Walk Metropolis Algorithms

2019 ◽  
Vol 2019 ◽  
pp. 1-24 ◽  
Author(s):  
Mylène Bédard
Keyword(s):  
Random Walk ◽  
Diffusion Process ◽  
Hierarchical Models ◽  
Real Data ◽  
Current Position ◽  
Proposal Distribution ◽  
Bayesian Analyses ◽  
Speed Measure ◽  
Random Walk Metropolis ◽  
Random Walk Metropolis Algorithm

We obtain weak convergence and optimal scaling results for the random walk Metropolis algorithm with a Gaussian proposal distribution. The sampler is applied to hierarchical target distributions, which form the building block of many Bayesian analyses. The global asymptotically optimal proposal variance derived may be computed as a function of the specific target distribution considered. We also introduce the concept of locally optimal tunings, i.e., tunings that depend on the current position of the Markov chain. The theorems are proved by studying the generator of the first and second components of the algorithm and verifying their convergence to the generator of a modified RWM algorithm and a diffusion process, respectively. The rate at which the algorithm explores its state space is optimized by studying the speed measure of the limiting diffusion process. We illustrate the theory with two examples. Applications of these results on simulated and real data are also presented.

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