Name
Abstract | ||
|---|---|---|
The FastInf C++ library is designed to perform memory and time efficient approximate inference in large-scale discrete undirected graphical models. The focus of the library is propagation based approximate inference methods, ranging from the basic loopy belief propagation algorithm to propagation based on convex free energies. Various message scheduling schemes that improve on the standard synchronous or asynchronous approaches are included. Also implemented are a clique tree based exact inference, Gibbs sampling, and the mean field algorithm. In addition to inference, FastInf provides parameter estimation capabilities as well as representation and learning of shared parameters. It offers a rich interface that facilitates extension of the basic classes to other inference and learning methods. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.5555/1756006.1859908 | Journal of Machine Learning Research |
Keywords | Field | DocType |
basic loopy belief propagation,clique tree,asynchronous approach,exact inference,basic class,gibbs sampling,mean field algorithm,approximate inference method,fastinf c,efficient approximate inference library,time efficient approximate inference | Computer science,Theoretical computer science,Approximate inference,Artificial intelligence,Statistical inference,Estimation theory,Adaptive neuro fuzzy inference system,Gibbs sampling,Belief propagation,Pattern recognition,Inference,Graphical model,Machine learning | Journal |
Volume | ISSN | Citations |
11, | 1532-4435 | 6 |
PageRank | References | Authors |
0.98 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ariel Jaimovich | 1 | 104 | 8.38 |
| Ofer Meshi | 2 | 154 | 12.94 |
| Ian McGraw | 3 | 253 | 24.41 |
| Elidan, Gal | 4 | 871 | 77.14 |