Adaptive Metropolis Hastings and Factor Slice Sampling

General purpose Markov Chain Monte Carlo sampling techniques for linearly correlated distribution

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.