conftrace_

markov chain monte carlo

725 papers

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Also known as

MCMC

Co-occurring keywords

bayesian inference (1907) variational inference (2069) langevin dynamics (128) hamiltonian monte carlo (92) gibbs sampling (201) stochastic gradient (296) mcmc sampling (70) mixing time (70) sampling algorithm (130) gaussian process (1200)

Papers

Perceptual Multistability as Markov Chain Monte Carlo Inference NIPS 2009
Multiscale Random Fields with Application to Contour Grouping NIPS 2008
Stochastic Relational Models for Large-scale Dyadic Data using MCMC NIPS 2008
Efficient Inference in Phylogenetic InDel Trees NIPS 2008
Efficient Sampling for Gaussian Process Inference using Control Variables NIPS 2008
A ``Shape Aware'' Model for semi-supervised Learning of Objects and its Context NIPS 2008
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems NIPS 2008
Non-stationary dynamic Bayesian networks NIPS 2008
The Gaussian Process Density Sampler NIPS 2008
The Infinite Gamma-Poisson Feature Model NIPS 2007
Multi-Task Learning for Classification with Dirichlet Process Priors JMLR 2007
Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains NIPS 2007
Markov Chain Monte Carlo with People NIPS 2007
Bayesian Policy Learning with Trans-Dimensional MCMC NIPS 2007
Semantic Modeling of Places using Objects RSS 2007
Collective Inference on Markov Models for Modeling Bird Migration NIPS 2007
Learning annotated hierarchies from relational data NIPS 2006
Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints NIPS 2006
Hierarchical Dirichlet Processes with Random Effects NIPS 2006
Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models NIPS 2006
Bayesian Ensemble Learning NIPS 2006
A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior JMLR 2005
Assessing Approximate Inference for Binary Gaussian Process Classification JMLR 2005
Reinforcement Learning with Factored States and Actions JMLR 2004
On Inclusion-Driven Learning of Bayesian Networks JMLR 2003