Sat, 26 Jul 2008 06:29:41 BST CiteULike: gabgas's probabilistic http://www.citeulike.org/user/gabgas/tag/probabilistic CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/gabgas/article/2194597 <i>ACM Computing Surveys, Vol. 33, No. 1. (2001), pp. 1-30.</i><br /><br />Many tasks require "reasoning" --- i.e., deriving conclusions from a corpus of explicitly stored information --- to solve their range of problems. An ideal reasoning system would produce alland -only the correct answers to every possible query, produce answers that are as specific as possible, be expressive enough to permit any possible fact to be stored and any possible query to be asked, and be efficient. Unfortunately, this is provably impossible: as correct and precise systems become more... Efficient reasoning Russell Greiner Christian Darken Iwan Santoso ACM Computing Surveys, Vol. 33, No. 1. (2001), pp. 1-30. 2008-01-04T13:50:05-00:00 2001 ACM Computing Surveys 33 1 1 30 learning logic machine probabilistic http://www.citeulike.org/user/gabgas/article/1541917 <i>Artificial Intelligence, Vol. 64, No. 1. (1993), pp. 81-129.</i><br /><br />This paper presents a simple framework for Horn-clause abduction, with probabilities associated with hypotheses. The framework incorporates assumptions about the rule base and independence assumptions amongst hypotheses. It is shown how any probabilistic knowledge representable in a discrete Bayesian belief network can be represented in this framework. The main contribution is in finding a relationship between logical and probabilistic notions of evidential reasoning. This provides a useful... Probabilistic Horn Abduction and Bayesian Networks David Poole Artificial Intelligence, Vol. 64, No. 1. (1993), pp. 81-129. 2007-08-07T19:53:22-00:00 1993 Artificial Intelligence 64 1 81 129 inference logic probabilistic http://www.citeulike.org/user/gabgas/article/259185 <i>Pattern Recognition, Vol. 38, No. 9. (September 2005), pp. 1349-1371.</i><br /><br />This article presents an overview of Probabilistic Automata (PA) and discrete Hidden Markov Models (HMMs), and aims at clarifying the links between them. The first part of this work concentrates on probability distributions generated by these models. Necessary and sufficient conditions for an automaton to define a probabilistic language are detailed. It is proved that probabilistic deterministic automata (PDFA) form a proper subclass of probabilistic non-deterministic automata (PNFA). Two families of equivalent models are described next. On one hand, HMMs and PNFA with no final probabilities generate distributions over complete finite prefix-free sets. On the other hand, HMMs with final probabilities and probabilistic automata generate distributions over strings of finite length. The second part of this article presents several learning models, which formalize the problem of PA induction or, equivalently, the problem of HMM topology induction and parameter estimation. These learning models include the PAC and identification with probability 1 frameworks. Links with Bayesian learning are also discussed. The last part of this article presents an overview of induction algorithms for PA or HMMs using state merging, state splitting, parameter pruning and error-correcting techniques. Links between probabilistic automata and hidden Markov models: probability distributions, learning models and induction algorithms P Dupont F Denis Y Esposito doi:10.1016/j.patcog.2004.03.020 Pattern Recognition, Vol. 38, No. 9. (September 2005), pp. 1349-1371. 2005-07-19T08:07:50-00:00 2005 Pattern Recognition 38 9 1349 1371 automata probabilistic