Importance sampling for families of distributions

[1]  L. Tierney.  A note on Metropolis-Hastings kernels for general state spaces , 1998 .

[2]  Radford M. Neal.  Sampling from multimodal distributions using tempered transitions , 1996, Stat. Comput..

[3]  P. Diaconis,et al.  LOGARITHMIC SOBOLEV INEQUALITIES FOR FINITE MARKOV CHAINS , 1996 .

[4]  M. Evans,et al.  Methods for Approximating Integrals in Statistics with Special Emphasis on Bayesian Integration Problems , 1995 .

[5]  Hani Doss.  Discussion: Markov Chains for Exploring Posterior Distributions , 1994 .

[6]  C. Geyer,et al.  Discussion: Markov Chains for Exploring Posterior Distributions , 1994 .

[7]  Jun S. Liu,et al.  Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes , 1994 .

[8]  Adrian F. M. Smith,et al.  Bayesian computation via the gibbs sampler and related markov chain monte carlo methods (with discus , 1993 .

[9]  Alistair Sinclair,et al.  Algorithms for Random Generation and Counting: A Markov Chain Approach , 1993, Progress in Theoretical Computer Science.

[10]  G. Parisi,et al.  Simulated tempering: a new Monte Carlo scheme , 1992, hep-lat/9205018.

[11]  J. P. Valleau,et al.  Density-scaling: a new Monte Carlo technique in statistical mechanics , 1991 .

[12]  B. Berg,et al.  Multicanonical algorithms for first order phase transitions , 1991 .

[13]  P. Diaconis,et al.  Geometric Bounds for Eigenvalues of Markov Chains , 1991 .

[14]  S. Caracciolo,et al.  Nonlocal Monte Carlo algorithm for self-avoiding walks with fixed endpoints , 1990 .

[15]  Alan M. Ferrenberg,et al.  New Monte Carlo technique for studying phase transitions. , 1988, Physical review letters.

[16]  S. Varadhan,et al.  Central limit theorem for additive functionals of reversible Markov processes and applications to simple exclusions , 1986 .

[17]  Leonard Gross,et al.  Decay of correlations in classical lattice models at high temperature , 1979 .

[18]  G. Torrie,et al.  Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling , 1977 .

[19]  Charles H. Bennett,et al.  Efficient estimation of free energy differences from Monte Carlo data , 1976 .

[20]  P. Peskun,et al.  Optimum Monte-Carlo sampling using Markov chains , 1973 .

[21]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[22]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[23]  N. Madras,et al.  Monte Carlo study of the ∵-point for collapsing trees , 1997 .

[24]  A. Sokal.  Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms , 1997 .

[25]  Xiao-Li Meng,et al.  SIMULATING RATIOS OF NORMALIZING CONSTANTS VIA A SIMPLE IDENTITY: A THEORETICAL EXPLORATION , 1996 .

[26]  Peter Green,et al.  Spatial statistics and Bayesian computation (with discussion) , 1993 .

[27]  C. Geyer.  Markov Chain Monte Carlo Maximum Likelihood , 1991 .

[28]  Andrew L. Rukhin,et al.  Tools for statistical inference , 1991 .

[29]  Robert H. Swendsen,et al.  Histogram Methods for Monte Carlo Data Analysis , 1990 .