Parameter Learning of Logic Programs for Symbolic-Statistical Modeling

Sato, Taisuke; Kameya, Yoshitaka

doi:10.1613/jair.912

Cited by 174 publications

(200 citation statements)

References 49 publications

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“…-Learning the structure and probabilities of belief networks [Heckerman, 1995]. There has been much work on learning parameters for the related system called PRISM [Sato and Kameya, 2001]. …”

Section: Icl and Learningmentioning

confidence: 99%

The Independent Choice Logic and Beyond

Poole

2008

Lecture Notes in Computer Science

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Abstract. The Independent Choice Logic began in the early 90's as a way to combine logic programming and probability into a coherent framework. The idea of the Independent Choice Logic is straightforward: there is a set of independent choices with a probability distribution over each choice, and a logic program that gives the consequences of the choices. There is a measure over possible worlds that is defined by the probabilities of the independent choices, and what is true in each possible world is given by choices made in that world and the logic program. ICL is interesting because it is a simple, natural and expressive representation of rich probabilistic models. This paper gives an overview of the work done over the last decade and half, and points towards the considerable work ahead, particularly in the areas of lifted inference and the problems of existence and identity.

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Section: Icl and Learningmentioning

confidence: 99%

The Independent Choice Logic and Beyond

Poole

2008

Lecture Notes in Computer Science

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“…In this section we will briefly introduce the PRISM programming language [9,10] and how to use it for Hidden Markov Models [1]. A PRISM-program consists of a logical and a probabilistic part.…”

Section: Prism and Hmm'smentioning

confidence: 99%

Probabilistic-Logical Modeling of Music

Sneyers

Vennekens

Schreye

2005

Practical Aspects of Declarative Languages

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Abstract. PRISM is a probabilistic-logical programming language based on Prolog. We present a PRISM-implementation of a general model for polyphonic music, based on Hidden Markov Models. Its probability parameters are automatically learned by running the built-in EM-algorithm of PRISM on training examples. We show how the model can be used as a classifier for music that guesses the composer of unknown fragments of music. Then we use it to automatically compose new music.

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“…PRISM [12,13] represents a logic-statistical modelling system that combines the logic programming language Prolog with probabilistic choice and machine learning, and is implemented as an extension to the B-Prolog language [16]. It includes discrete random variables called multi-valued random switches, abbreviated msw's.…”

Section: Logic-statistical Modelling In Prismmentioning

confidence: 99%

“…PRISM uses some quite advanced algorithms and data structures in order to do this in an efficient way; these topics are outside the scope of this paper and we refer to [12,13]. Training with a single observation (somewhat artificial) being the short annotated sequence above, we get for t1 prob.…”

Section: Values(nextsubstringtype(_)[t1t2t3]) Values(continueseqmentioning

confidence: 99%

“…This background sequence, however, is constructed in rather simplified, using a Markov Model to generate GC content of 38%. The present work is intended as a further step in this direction, demonstrating how the logic-statistical machine-learning system PRISM, introduced by other authors [12,13], can be used for the development of more sophisticated and reliable models. Based on logic programming extended with random variables and parameter learning, PRISM appears as a powerful modelling environment, which subsumes HMMs and a wide range of other methods, all embedded in a declarative language.…”

Section: Introductionmentioning

confidence: 96%

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A Machine Learning Approach to Test Data Generation: A Case Study in Evaluation of Gene Finders

Christiansen

Dahmcke

Machine Learning and Data Mining in Pattern Recognition

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Abstract. Programs for gene prediction in computational biology are examples of systems for which the acquisition of authentic test data is difficult as these require years of extensive research. This has lead to test methods based on semiartificially produced test data, often produced by ad hoc techniques complemented by statistical models such as Hidden Markov Models (HMM). The quality of such a test method depends on how well the test data reflect the regularities in known data and how well they generalize these regularities. So far only very simplified and generalized, artificial data sets have been tested, and a more thorough statistical foundation is required. We propose to use logic-statistical modelling methods for machine-learning for analyzing existing and manually marked up data, integrated with the generation of new, artificial data. More specifically, we suggest to use the PRISM system developed by Sato and Kameya. Based on logic programming extended with random variables and parameter learning, PRISM appears as a powerful modelling environment, which subsumes HMMs and a wide range of other methods, all embedded in a declarative language. We illustrate these principles here, showing parts of a model under development for genetic sequences and indicate first initial experiments producing test data for evaluation of existing gene finders, exemplified by GENSCAN, HMMGene and genemark.hmm.

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Parameter Learning of Logic Programs for Symbolic-Statistical Modeling

Cited by 174 publications

References 49 publications

The Independent Choice Logic and Beyond

The Independent Choice Logic and Beyond

Probabilistic-Logical Modeling of Music

A Machine Learning Approach to Test Data Generation: A Case Study in Evaluation of Gene Finders

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