Adaptive Metropolis Hastings and Factor Slice Sampling
General purpose Markov Chain Monte Carlo sampling techniques for linearly correlated distribution
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This package contains 3 MCMC functions:
RWM.m - Standard Metropolis Hastings with optimal acceptance rate tuning. [N.Metropolis,A.W.Rosenbluth,M.N.Rosenbluth,A.H.Teller,and E. Teller, “Equations of state calculations by fast computing machine,” Journal of Chemical Physics, vol. 21, no. 6, pp. 1087–1092, 1953]
AM.m - Adaptive Metropolis with optimal acceptance rate tuning. [H. Haario, E. Saksman, and J. Tamminen, “An adaptive Metropolis algorithm,” Bernoulli, vol. 7, pp. 223–242, 2001]
FSS.m - Factor Slice Sampling with optimal initial width tuning. [M. M. Tibbits, C. Groendyke, M. Haran, and J. C. Liechty, “Automated Factor Slice Sampling,” Journal of Computational and Graphical Statistics, no. April 2013, Apr. 2013]
Usage of these functions is illustrated in "demo_mcmc.m" with a spherical Gaussian target distribution. These procedures, however, can work well for arbitrary target density given that the correlation between all dimensions is linear.
인용 양식
Khoa Tran (2026). Adaptive Metropolis Hastings and Factor Slice Sampling (https://kr.mathworks.com/matlabcentral/fileexchange/45976-adaptive-metropolis-hastings-and-factor-slice-sampling), MATLAB Central File Exchange. 검색 날짜: .
