bayesian inference

Global Sensitivity Analysis for MAP Inference in Graphical Models NeurIPS 2014 Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models NeurIPS 2014 Hardness of parameter estimation in graphical models NeurIPS 2014 Improving Prediction from Dirichlet Process Mixtures via Enrichment JMLR 2014 Incremental Tree-Based Inference with Dependent Normalized Random Measures AISTATS 2014 Information-based learning by agents in unbounded state spaces NeurIPS 2014 Joint Structure Learning of Multiple Non-Exchangeable Networks AISTATS 2014 Kernel Adaptive Metropolis-Hastings ICML 2014 Latent Confusion Analysis by Normalized Gamma Construction ICML 2014 Latent Gaussian Models for Topic Modeling AISTATS 2014 Learning Modular Structures from Network Data and Node Variables ICML 2014 Learning the Parameters of Determinantal Point Process Kernels ICML 2014 Locally Adaptive Factor Processes for Multivariate Time Series JMLR 2014 Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians ICML 2014 Memory (and Time) Efficient Sequential Monte Carlo ICML 2014 Multi-scale Graphical Models for Spatio-Temporal Processes NeurIPS 2014 Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks NeurIPS 2014 Non-Parametric Bayesian Constrained Local Models CVPR 2014 Nonparametric Bayesian inference on multivariate exponential families NeurIPS 2014 Online Bayesian Passive-Aggressive Learning ICML 2014 On Prior Distributions and Approximate Inference for Structured Variables NeurIPS 2014 Optimal prior-dependent neural population codes under shared input noise NeurIPS 2014 Parallel MCMC with Generalized Elliptical Slice Sampling JMLR 2014 Particle Gibbs with Ancestor Sampling JMLR 2014 Pitfalls in the use of Parallel Inference for the Dirichlet Process ICML 2014