bayesian inference

Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data NeurIPS 2014 Polya-gamma augmentations for factor models ACML 2014 Probabilistic ODE Solvers with Runge-Kutta Means NeurIPS 2014 Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics AISTATS 2014 Robust RegBayes: Selectively Incorporating First-Order Logic Domain Knowledge into Bayesian Models ICML 2014 Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors ICML 2014 Scalable Collaborative Bayesian Preference Learning AISTATS 2014 Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications ICML 2014 Scalable Inference for Neuronal Connectivity from Calcium Imaging NeurIPS 2014 Scaling Nonparametric Bayesian Inference via Subsample-Annealing AISTATS 2014 Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models NeurIPS 2014 Semi-supervised Learning with Deep Generative Models NeurIPS 2014 Sequential Monte Carlo for Graphical Models NeurIPS 2014 SMERED: A Bayesian Approach to Graphical Record Linkage and De-duplication AISTATS 2014 Smoothed Gradients for Stochastic Variational Inference NeurIPS 2014 Sparse Bayesian structure learning with “dependent relevance determination” priors NeurIPS 2014 Spatio-temporal Representations of Uncertainty in Spiking Neural Networks NeurIPS 2014 Spectral Methods for Indian Buffet Process Inference NeurIPS 2014 Spherical Hamiltonian Monte Carlo for Constrained Target Distributions ICML 2014 Stochastic Gradient Hamiltonian Monte Carlo ICML 2014 Stochastic Regret Minimization via Thompson Sampling COLT 2014 Stochastic Variational Inference for Bayesian Time Series Models ICML 2014 Stochastic variational inference for hidden Markov models NeurIPS 2014 The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling AISTATS 2014 The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo JMLR 2014