Sciweavers

Free Online Productivity Tools i2Speak i2Symbol i2OCR iTex2Img iWeb2Print iWeb2Shot i2Type iPdf2Split iPdf2Merge i2Bopomofo i2Arabic i2Style i2Image i2PDF iLatex2Rtf Sci2ools

107

Voted

SAC
2015
ACM

20views Applied Computing» more SAC 2015»

Cuts in Bayesian graphical models

10 years 24 days ago

Cuts in Bayesian graphical models

Download www.wu.ac.at

Abstract The cut function deﬁned by the OpenBUGS software is described as a “valve” that prevents feedback in Bayesian graphical models. It is shown that the MCMC algorithm applied by OpenBUGS in the presence of a cut function does not converge to a well-deﬁned limiting distribution. However, it may be improved by using tempered transitions. The cut algorithm is compared with multiple imputation as a gold standard in a simple example. Keywords Bayesian inference · Cutting feedback · Multiple imputation

Martyn Plummer

Real-time Traffic

Applied Computing | SAC 2015 |

claim paper

Related Content

» A Graphical Framework for Spectrum Modeling and Decision Making in Cognitive Radio Network...

» Pairwise Clustering and Graphical Models

» Multiagent Bayesian Forecasting of Time Series with Graphical Models

» Shot detection combining Bayesian and structural information

» Bayesian Learning in Undirected Graphical Models Approximate MCMC Algorithms

» Bayesian Learning in Sparse Graphical Factor Models via Variational MeanField Annealing

» Bayesian Shot Detection Using Structural Weighting

» Bayesian Oil Spill Segmentation of SAR Images Via Graph Cuts

» Near RealTime Motion Segmentation Using Graph Cuts

» Iterative Decoding of Compound Codes by Probability Propagation in Graphical Models

Post Info
More Details (n/a)

Added	17 Apr 2016
Updated	17 Apr 2016
Type	Journal
Year	2015
Where	SAC
Authors	Martyn Plummer

Comments (0)