user- HBC is a toolkit for implementing hierarchical Bayesian models.
- In order to better understand complex Belief-Propagation models tested with our simulator we have identified a strong need for a simple visualization tool ...In order to better understand complex Belief-Propagation models tested with our simulator we have identified a strong need for a simple visualization tool that will grant us insight of the tested graphs.
- "We explain the principles behind the belief propagation (BP) algorithm, which is an efficient way to solve inference problems based on passing local messa..."We explain the principles behind the belief propagation (BP) algorithm, which is an efficient way to solve inference problems based on passing local messages."
- Judea Pearl is one of the pioneers of Bayesian networks and the probabilistic approach to artificial intelligence. Invented the belief propagation algorith...Judea Pearl is one of the pioneers of Bayesian networks and the probabilistic approach to artificial intelligence. Invented the belief propagation algorithm.
- "Maybe you're a girl looking for a boyfriend, but the boy you're interested in refuses to date anyone who "isn't Bayesian". What matters is that Bayes is ..."Maybe you're a girl looking for a boyfriend, but the boy you're interested in refuses to date anyone who "isn't Bayesian". What matters is that Bayes is cool, and if you don't know Bayes, you aren't cool."
- As the use of a Bayesian probability calculation on a simple co-occurrence frequency table created from the same data has similar disambiguation capabiliti...As the use of a Bayesian probability calculation on a simple co-occurrence frequency table created from the same data has similar disambiguation capabilities, the paper also incorporates comparison of LSA with the Bayesian model.
- In this paper, we perform WSD study based on large scale real-world corpus using two unsupervised learning algorithms based on ±n-improved Bayesian model a...In this paper, we perform WSD study based on large scale real-world corpus using two unsupervised learning algorithms based on ±n-improved Bayesian model and Dependency Grammar (DG)-improved Bayesian model.
- This paper describes an experimental comparison between two standard supervised learning methods, namely Naive Bayes and Exemplar--based classification, on...This paper describes an experimental comparison between two standard supervised learning methods, namely Naive Bayes and Exemplar--based classification, on the Word Sense Disambiguation (WSD) problem.
- This is HTM: What extends the expressive power of Hierarchical Bayesian Networks is that a node may correspond to an aggregation of simpler types.
- In this paper we propose the type of Bayesian networks that we call the hierarchical Bayesian network (HBN) classifiers. We present algorithms for the cons...In this paper we propose the type of Bayesian networks that we call the hierarchical Bayesian network (HBN) classifiers. We present algorithms for the construction of the HBN classifiers and test them on the Reuters text categorization test collection
- This list is intended to introduce some of the tools of Bayesian statistics and machine learning that can be useful to computational research in cognitive ...This list is intended to introduce some of the tools of Bayesian statistics and machine learning that can be useful to computational research in cognitive science.
- Proceedings of the PRICAI Workshop on Text and Web Mining, page 36--43. (2000)
- Proceedings of the American Association of Artificial Intelligence National Conference on AI, page 133--136. Pittsburgh, PA, (1982)
- Proceedings of the IJCAI-83, page 190--193. Karlsruhe, Germany, (1983)
- Morgan Kaufmann, (September 1988)
