markov chain monte carlo

Variational Inference via χ Upper Bound Minimization NeurIPS 2017 Zonotope Hit-and-run for Efficient Sampling from Projection DPPs ICML 2017 A Gibbs Sampler for Learning DAGs JMLR 2016 A Kernel Test of Goodness of Fit ICML 2016 Bayesian Generalised Ensemble Markov Chain Monte Carlo AISTATS 2016 Bayesian Graphical Models for Multivariate Functional Data JMLR 2016 Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models JMLR 2016 Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization AISTATS 2016 C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching AISTATS 2016 Completely random measures for modelling block-structured sparse networks NeurIPS 2016 Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics JMLR 2016 Coresets for Scalable Bayesian Logistic Regression NeurIPS 2016 Diversity-Promoting Bayesian Learning of Latent Variable Models ICML 2016 Efficient Point Process Inference for Large-Scale Object Detection CVPR 2016 Electronic Health Record Analysis via Deep Poisson Factor Models JMLR 2016 Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling ICML 2016 Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics JMLR 2016 Fast and Provably Good Seedings for k-Means NeurIPS 2016 Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling NeurIPS 2016 Gaussian Processes for Survival Analysis NeurIPS 2016 Hawkes Processes with Stochastic Excitations ICML 2016 Improving Structure MCMC for Bayesian Networks through Markov Blanket Resampling JMLR 2016 Interacting Particle Markov Chain Monte Carlo ICML 2016 Launch and Iterate: Reducing Prediction Churn NeurIPS 2016 Learning Parameters of Hybrid Time Bayesian Networks PGM 2016