2014/01/15 by Shaan Qamar, Qamar, Shaan, Rajarshi Guhaniyogi +3 · 1 citation
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistical Methods and Inference #cs.LG #stat.CO #stat.ML
paper · pdf · doi:10.48550/arxiv.1401.3632
41 pages, 7 figures, 12 tables
openalex publication_date 2014/01/15 · arxiv created 2015/09/22 · arxiv updated 2015/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a Conditional Density Filtering (C-DF) algorithm for efficient online Bayesian inference. C-DF adapts MCMC sampling to the online setting, sampling from approximations to conditional posterior distributions obtained by propagating surrogate conditional sufficient statistics (a function of data and parameter estimates) as new data arrive. These quantities eliminate the need to store or process the entire dataset simultaneously and offer a number of desirable features. Often, these include a reduction in memory requirements and runtime and improved mixing, along with state-of-the-art parameter inference and prediction. These improvements are demonstrated through several illustrative examples including an application to high dimensional compressed regression. Finally, we show that C-DF samples converge to the target posterior distribution asymptotically as sampling proceeds and more data arrives.