Bayesian Generalised Ensemble Markov Chain Monte Carlo

JF acknowledge funding from the Danish Council for Independent Research | Natural Sciences. ZG acknowledge funding from EPSRC EP/I036575/1 and Google.

[1]  Iain Murray Advances in Markov chain Monte Carlo methods , 2007 .

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

[3]  F. Y. Wu The Potts model , 1982 .

[4]  K. Binder,et al.  A Guide to Monte Carlo Simulations in Statistical Physics , 2000 .

[5]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[6]  Xiao-Li Meng,et al.  Simulating Normalizing Constants: From Importance Sampling to Bridge Sampling to Path Sampling , 1998 .

[7]  Hesselbo,et al.  Monte Carlo simulation and global optimization without parameters. , 1995, Physical review letters.

[8]  Lee,et al.  New Monte Carlo algorithm: Entropic sampling. , 1993, Physical review letters.

[9]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[10]  STUDIES OF AN OFF-LATTICE MODEL FOR PROTEIN FOLDING: SEQUENCE DEPENDENCE AND IMPROVED SAMPLING AT FINITE TEMPERATURE , 1995, chem-ph/9505003.

[11]  J. W. Neidigh,et al.  Designing a 20-residue protein , 2002, Nature Structural Biology.

[12]  Anders Irbäck,et al.  PROFASI: A Monte Carlo simulation package for protein folding and aggregation , 2006, J. Comput. Chem..

[13]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[14]  Berg,et al.  Multicanonical ensemble: A new approach to simulate first-order phase transitions. , 1992, Physical review letters.

[15]  Radford M. Neal Annealed importance sampling , 1998, Stat. Comput..

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

[17]  A. Lyubartsev,et al.  New approach to Monte Carlo calculation of the free energy: Method of expanded ensembles , 1992 .

[18]  Tijmen Tieleman,et al.  Training restricted Boltzmann machines using approximations to the likelihood gradient , 2008, ICML '08.

[19]  Wang,et al.  Replica Monte Carlo simulation of spin glasses. , 1986, Physical review letters.

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

[21]  Ruslan Salakhutdinov,et al.  Learning Deep Boltzmann Machines using Adaptive MCMC , 2010, ICML.

[22]  Y. Okamoto,et al.  GENERALIZED-ENSEMBLE MONTE CARLO METHOD FOR SYSTEMS WITH ROUGH ENERGY LANDSCAPE , 1997, cond-mat/9710306.

[23]  D. Landau,et al.  Efficient, multiple-range random walk algorithm to calculate the density of states. , 2000, Physical review letters.

[24]  J. Ferkinghoff-Borg Monte Carlo Methods for Inference in High-Dimensional Systems , 2012 .

[25]  J. Skilling Nested sampling for general Bayesian computation , 2006 .

[26]  Ruslan Salakhutdinov,et al.  Annealing between distributions by averaging moments , 2013, NIPS.

[27]  A. Rollett,et al.  The Monte Carlo Method , 2004 .

[28]  Beale Exact distribution of energies in the two-dimensional ising model. , 1996, Physical review letters.

[29]  J. Ferkinghoff-Borg,et al.  Optimized Monte Carlo analysis for generalized ensembles , 2002 .

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

[31]  G. Wahba Improper Priors, Spline Smoothing and the Problem of Guarding Against Model Errors in Regression , 1978 .

[32]  Carl E. Rasmussen,et al.  A Unifying View of Sparse Approximate Gaussian Process Regression , 2005, J. Mach. Learn. Res..

[33]  Yukito Iba EXTENDED ENSEMBLE MONTE CARLO , 2001 .

[34]  Zoubin Ghahramani,et al.  Nested sampling for Potts models , 2005, NIPS.