Bayesian Inference

Generating Efficient MCMC Kernels from Probabilistic Programs AISTATS 2014 Global Sensitivity Analysis for MAP Inference in Graphical Models NeurIPS 2014 Ground Plane Estimation using a Hidden Markov Model CVPR 2014 Hamiltonian Monte Carlo Without Detailed Balance ICML 2014 Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models NeurIPS 2014 Hierarchical Dirichlet Scaling Process ICML 2014 Improving Prediction from Dirichlet Process Mixtures via Enrichment JMLR 2014 Inconsistency of Pitman-Yor Process Mixtures for the Number of Components JMLR 2014 Incremental Tree-Based Inference with Dependent Normalized Random Measures AISTATS 2014 Inferning with High Girth Graphical Models ICML 2014 Inferring synaptic conductances from spike trains with a biophysically inspired point process model NeurIPS 2014 Information-based learning by agents in unbounded state spaces NeurIPS 2014 Input Warping for Bayesian Optimization of Non-Stationary Functions ICML 2014 Interval Tracker: Tracking by Interval Analysis CVPR 2014 Iterative Neural Autoregressive Distribution Estimator NADE-k NeurIPS 2014 Joint Structure Learning of Multiple Non-Exchangeable Networks AISTATS 2014 Just-In-Time Learning for Fast and Flexible Inference NeurIPS 2014 Kernel Adaptive Metropolis-Hastings ICML 2014 LAMORE: A Stable, Scalable Approach to Latent Vector Autoregressive Modeling of Categorical Time Series AISTATS 2014 Latent Gaussian Models for Topic Modeling AISTATS 2014 Learning Bounded Tree-width Bayesian Networks using Integer Linear Programming AISTATS 2014 Learning Modular Structures from Network Data and Node Variables ICML 2014 Learning Sum-Product Networks with Direct and Indirect Variable Interactions ICML 2014 Learning the Irreducible Representations of Commutative Lie Groups ICML 2014 Learning the Parameters of Determinantal Point Process Kernels ICML 2014