Continuously Tempered Hamiltonian Monte Carlo
暂无分享,去创建一个
[1] W. K. Hastings,et al. Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .
[2] Joshua B. Tenenbaum,et al. Human-level concept learning through probabilistic program induction , 2015, Science.
[3] Stochastic Relaxation , 2014, Computer Vision, A Reference Guide.
[4] Ernst Hairer,et al. Simulating Hamiltonian dynamics , 2006, Math. Comput..
[5] M. Betancourt,et al. Adiabatic Monte Carlo , 2014 .
[6] Radford M. Neal. MCMC Using Hamiltonian Dynamics , 2011, 1206.1901.
[7] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[8] John Salvatier,et al. Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.
[9] David E. Carlson,et al. Partition Functions from Rao-Blackwellized Tempered Sampling , 2016, ICML.
[10] Xiao-Li Meng,et al. Simulating Normalizing Constants: From Importance Sampling to Bridge Sampling to Path Sampling , 1998 .
[11] Radford M. Neal. Sampling from multimodal distributions using tempered transitions , 1996, Stat. Comput..
[12] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[13] S. Duane,et al. Hybrid Monte Carlo , 1987 .
[14] Joseph Hilbe,et al. Data Analysis Using Regression and Multilevel/Hierarchical Models , 2009 .
[15] P. Damlen,et al. Gibbs sampling for Bayesian non‐conjugate and hierarchical models by using auxiliary variables , 1999 .
[16] Michael Betancourt,et al. A General Metric for Riemannian Manifold Hamiltonian Monte Carlo , 2012, GSI.
[17] Gianpaolo Gobbo,et al. Extended Hamiltonian approach to continuous tempering. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.
[18] Simon Haykin,et al. GradientBased Learning Applied to Document Recognition , 2001 .
[19] Andrew Gelman,et al. The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo , 2011, J. Mach. Learn. Res..
[20] Ruslan Salakhutdinov,et al. On the quantitative analysis of deep belief networks , 2008, ICML '08.
[21] Dustin Tran,et al. Variational Inference via \chi Upper Bound Minimization , 2016, NIPS.
[22] Donald Geman,et al. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[23] George Casella,et al. A Short History of Markov Chain Monte Carlo: Subjective Recollections from Incomplete Data , 2008, 0808.2902.
[24] Ruslan Salakhutdinov,et al. On the Quantitative Analysis of Decoder-Based Generative Models , 2016, ICLR.
[25] Ryan P. Adams,et al. Sandwiching the marginal likelihood using bidirectional Monte Carlo , 2015, ArXiv.
[26] C. Geyer. Markov Chain Monte Carlo Maximum Likelihood , 1991 .
[27] Adrian F. M. Smith,et al. Sampling-Based Approaches to Calculating Marginal Densities , 1990 .
[28] Dustin Tran,et al. Automatic Differentiation Variational Inference , 2016, J. Mach. Learn. Res..
[29] G. Parisi,et al. Simulated tempering: a new Monte Carlo scheme , 1992, hep-lat/9205018.
[30] Radford M. Neal. Slice Sampling , 2003, The Annals of Statistics.
[31] A. Laio,et al. Escaping free-energy minima , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[32] Wang,et al. Replica Monte Carlo simulation of spin glasses. , 1986, Physical review letters.
[33] C. Geyer,et al. Annealing Markov chain Monte Carlo with applications to ancestral inference , 1995 .
[34] O. Zobay. Mean field inference for the Dirichlet process mixture model , 2009 .
[35] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[36] Nando de Freitas,et al. Variational MCMC , 2001, UAI.
[37] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[38] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[39] Dustin Tran,et al. The $χ$-Divergence for Approximate Inference , 2016, ArXiv.
[40] Max Welling,et al. Markov Chain Monte Carlo and Variational Inference: Bridging the Gap , 2014, ICML.
[41] Jiqiang Guo,et al. Stan: A Probabilistic Programming Language. , 2017, Journal of statistical software.
[42] Nial Friel,et al. Tuning tempered transitions , 2010, Stat. Comput..
[43] Richard E. Turner,et al. Rényi Divergence Variational Inference , 2016, NIPS.
[44] John Salvatier,et al. Probabilistic programming in Python using PyMC3 , 2016, PeerJ Comput. Sci..
[45] Michael W Deem,et al. Parallel tempering: theory, applications, and new perspectives. , 2005, Physical chemistry chemical physics : PCCP.
[46] Radford M. Neal. Annealed importance sampling , 1998, Stat. Comput..
[47] Tom Minka,et al. Expectation Propagation for approximate Bayesian inference , 2001, UAI.
[48] Massimiliano Bonomi,et al. Reconstructing the equilibrium Boltzmann distribution from well‐tempered metadynamics , 2009, J. Comput. Chem..
[49] Ruslan Salakhutdinov,et al. Importance Weighted Autoencoders , 2015, ICLR.
[50] Yichuan Zhang,et al. Continuous Relaxations for Discrete Hamiltonian Monte Carlo , 2012, NIPS.