Skip to content
conftrace_

stochastic process

427 papers

SP

Papers

Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese Restaurant Process Approach AISTATS 2012 Coding efficiency and detectability of rate fluctuations with non-Poisson neuronal firing NeurIPS 2012 Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model NeurIPS 2012 Interpreting prediction markets: a stochastic approach NeurIPS 2012 Learning with Partially Absorbing Random Walks NeurIPS 2012 Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs AISTATS 2012 Slice sampling normalized kernel-weighted completely random measure mixture models NeurIPS 2012 Inference in continuous-time change-point models NeurIPS 2011 Nonstandard Interpretations of Probabilistic Programs for Efficient Inference NeurIPS 2011 On the Relation between Realizable and Nonrealizable Cases of the Sequence Prediction Problem JMLR 2011 Statistical Optimization of Non-Negative Matrix Factorization AISTATS 2011 The Indian Buffet Process: An Introduction and Review JMLR 2011 Approximate parameter inference in a stochastic reaction-diffusion model AISTATS 2010 Mean Field Variational Approximation for Continuous-Time Bayesian Networks JMLR 2010 On Finding Predictors for Arbitrary Families of Processes JMLR 2010 Risk Bounds for Levy Processes in the PAC-Learning Framework AISTATS 2010 Stochastic Modeling of the Expected Time to Search for an Intermittent Signal Source Under a Limited Sensing Range RSS 2010 Construction of Nonparametric Bayesian Models from Parametric Bayes Equations NeurIPS 2009 Algorithms for Infinitely Many-Armed Bandits NeurIPS 2008 Metastable Walking on Stochastically Rough Terrain RSS 2008 Rademacher Complexity Bounds for Non-I.I.D. Processes NeurIPS 2008 The Mondrian Process NeurIPS 2008 Value Function Based Reinforcement Learning in Changing Markovian Environments JMLR 2008 Discovering Weakly-Interacting Factors in a Complex Stochastic Process NeurIPS 2007 Inferring Elapsed Time from Stochastic Neural Processes NeurIPS 2007