Name
Abstract | ||
|---|---|---|
Continuous quantities are ubiquitous in models of real-world phenomena, but are surprisingly difficult to reason about automatically. Probabilistic graphical models such as Bayesian networks and Markov random fields, and algorithms for approximate inference such as belief propagation (BP), have proven to be powerful tools in a wide range of applications in statistics and artificial intelligence. However, applying these methods to models with continuous variables remains a challenging task. In this work we describe an extension of BP to continuous variable models, generalizing particle filtering, and Gaussian mixture filtering techniques for time series to more complex models. We illustrate the power of the resulting nonparametric BP algorithm via two applications: kinematic tracking of visual motion and distributed localization in sensor networks. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1831407.1831431 | Commun. ACM |
Keywords | Field | DocType |
markov random field,nonparametric belief propagation,continuous likelihood,challenging task,bayesian network,gaussian mixture,computer vision,nbp iteration,continuous variable,nbp algorithm,regularized particle filter,graphical model,nonparametric bp algorithm,artificial intelligence,inference algorithm,continuous distribution,belief propagation,continuous variable model,continuous quantity,general vision problem,particle filter | Computer science,Markov chain,Particle filter,Algorithm,Nonparametric statistics,Theoretical computer science,Approximate inference,Bayesian network,Gaussian process,Artificial intelligence,Graphical model,Belief propagation | Journal |
Volume | Issue | ISSN |
53 | 10 | 0001-0782 |
ISBN | Citations | PageRank |
0-7695-1900-8 | 171 | 13.45 |
References | Authors | |
51 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Erik B. Sudderth | 1 | 1420 | 119.04 |
| Alexander T. Ihler | 2 | 1377 | 112.01 |
| Michael Isard | 3 | 9533 | 729.89 |
| William T. Freeman | 4 | 17382 | 1968.76 |
| Alan S. Willsky | 5 | 7466 | 847.01 |