Markov Chain Monte Carlo

Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path NeurIPS 2015 Neural Adaptive Sequential Monte Carlo NeurIPS 2015 On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators NeurIPS 2015 Parallelizing MCMC with Random Partition Trees NeurIPS 2015 Particle Gibbs for Infinite Hidden Markov Models NeurIPS 2015 Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width NeurIPS 2015 Reflection, Refraction, and Hamiltonian Monte Carlo NeurIPS 2015 Sampling from Probabilistic Submodular Models NeurIPS 2015 Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring NeurIPS 2015 Transformation of Markov Random Fields for Marginal Distribution Estimation CVPR 2015 Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models NeurIPS 2015 Variational Consensus Monte Carlo NeurIPS 2015 A Mixture of Manhattan Frames: Beyond the Manhattan World CVPR 2014 Asynchronous Anytime Sequential Monte Carlo NeurIPS 2014 Bayesian Sampling Using Stochastic Gradient Thermostats NeurIPS 2014 Distributed Bayesian Posterior Sampling via Moment Sharing NeurIPS 2014 Fast Sampling-Based Inference in Balanced Neuronal Networks NeurIPS 2014 Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models NeurIPS 2014 Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks NeurIPS 2014 Parallel Sampling of HDPs using Sub-Cluster Splits NeurIPS 2014 PEWA: Patch-based Exponentially Weighted Aggregation for image denoising NeurIPS 2014 Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data NeurIPS 2014 Positive Curvature and Hamiltonian Monte Carlo NeurIPS 2014 Projecting Markov Random Field Parameters for Fast Mixing NeurIPS 2014 Scaling Nonparametric Bayesian Inference via Subsample-Annealing AISTATS 2014