Probabilistic-Logical Modeling of Music

Sneyers, Jon; Vennekens, Joost; Schreye, Danny De

doi:10.1007/11603023_5

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Specifically it offers, in addition to MLE by the EM algorithm, Viterbi training (VT), variational Bayes (VB), variational VT (VB-VT) and MCMC for Bayesian inference. PRISM has been applied to music and bioinformatics [25,1,18]. Syntactically a PRISM program DB is a Prolog program and runs like Prolog.…”

Section: Prism At a Glancementioning

confidence: 99%

A Logic-based Approach to Generatively Defined Discriminative Modeling

Sato¹,

Kubota²,

Kameya³

2014

Preprint

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Conditional random fields (CRFs) are usually specified by graphical models but in this paper we propose to use probabilistic logic programs and specify them generatively. Our intension is first to provide a unified approach to CRFs for complex modeling through the use of a Turing complete language and second to offer a convenient way of realizing generative-discriminative pairs in machine learning to compare generative and discriminative models and choose the best model. We implemented our approach as the D-PRISM language by modifying PRISM, a logic-based probabilistic modeling language for generative modeling, while exploiting its dynamic programming mechanism for efficient probability computation. We tested D-PRISM with logistic regression, a linear-chain CRF and a CRF-CFG and empirically confirmed their excellent discriminative performance compared to their generative counterparts, i.e. naive Bayes, an HMM and a PCFG. We also introduced new CRF models, CRF-BNCs and CRF-LCGs. They are CRF versions of Bayesian network classifiers and probabilistic left-corner grammars respectively and easily implementable in D-PRISM. We empirically showed that they outperform their generative counterparts as expected.

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Section: Prism At a Glancementioning

confidence: 99%

A Logic-based Approach to Generatively Defined Discriminative Modeling

Sato¹,

Kubota²,

Kameya³

2014

Preprint

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show abstract

“…Another interesting application area is the automatic analysis and generation of music. In the past, we have used PRISM to analyse and generate music in a probabilistic setting (Sneyers et al 2006). There are also several deterministic approaches based on constraints and strict rules (e.g.…”

Section: Related Workmentioning

confidence: 99%

CHR(PRISM)-based probabilistic logic learning

Sneyers

Meert

Vennekens

et al. 2010

Theory and Practice of Logic Programming

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PRISM is an extension of Prolog with probabilistic predicates and built-in support for expectation-maximization learning. Constraint Handling Rules (CHR) is a high-level programming language based on multi-headed multiset rewrite rules.In this paper, we introduce a new probabilistic logic formalism, called CHRiSM, based on a combination of CHR and PRISM. It can be used for high-level rapid prototyping of complex statistical models by means of "chance rules". The underlying PRISM system can then be used for several probabilistic inference tasks, including probability computation and parameter learning. We define the CHRiSM language in terms of syntax and operational semantics, and illustrate it with examples. We define the notion of ambiguous programs and define a distribution semantics for unambiguous programs. Next, we describe an implementation of CHRiSM, based on CHR(PRISM). We discuss the relation between CHRiSM and other probabilistic logic programming languages, in particular PCHR. Finally, we identify potential application domains.

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Improving the Outcome of a Probabilistic Logic Music System Generator by Using Perlin Noise

Nicholson¹,

Schreye

Sneyers

2011

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Probabilistic-Logical Modeling of Music

Cited by 3 publications

References 5 publications

A Logic-based Approach to Generatively Defined Discriminative Modeling

A Logic-based Approach to Generatively Defined Discriminative Modeling

CHR(PRISM)-based probabilistic logic learning

Improving the Outcome of a Probabilistic Logic Music System Generator by Using Perlin Noise

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