Results 221 to 230 of about 81,262 (253)

Dynamic causal modelling in probabilistic programming languages. [PDF]

J R Soc Interface
Baldy N, Woodman M, Jirsa VK, Hashemi M.
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Hybrid Probabilistic Logic Programs as Residuated Logic Programs

Studia Logica, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Damásio, Carlos Viegas, Pereira, Luís Moniz   +1 more
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Agent-Oriented Probabilistic Logic Programming

Journal of Computer Science and Technology, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Jie, Ju, Shi-Er, Liu, Chun-Nian
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Probabilistic Logic Programming in Action

2017
Probabilistic Programming (PP) has recently emerged as an effective approach for building complex probabilistic models. Until recently PP was mostly focused on functional programming while now Probabilistic Logic Programming (PLP) forms a significant subfield.
Arnaud, Nguembang Fadja, Riguzzi, Fabrizio   +1 more
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Quantum probabilistic logic programming

SPIE Proceedings, 2015
We describe a quantum mechanics based logic programming language that supports Horn clauses, random variables, and covariance matrices to express and solve problems in probabilistic logic. The Horn clauses of the language wrap random variables, including infinite valued, to express probability distributions and statistical correlations, a powerful ...
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Probabilistic Functional Logic Programming

2017
This paper presents PFLP, a library for probabilistic programming in the functional logic programming language Curry. It demonstrates how the concepts of a functional logic programming language support the implementation of a library for probabilistic programming.
Sandra Dylus, Jan Christiansen, Finn Teegen   +2 more
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Abduction in Probabilistic Logic Programs

2022
The representation of scenarios drawn from real-world domains is certainly favored by the presence of simple but powerful languages capable of capturing all facets of the problem. Probabilistic Logic Programming (PLP) [5,11] plays a fundamental role in this thanks to its ability to represent uncertain and complex information [3,10] and the possibility ...
Azzolini D., Bellodi E., Ferilli S., Riguzzi F., Zese R.   +4 more
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Probabilistic Logic Programming (PLP 2017)

International Journal of Approximate Reasoning, 2019
Probabilistic Logic Programming (PLP) has come to the fore in the last decades as one of the most promising ways to model and reason on many different domains, including for example bioinformatics, semantic web, robotics, and computer vision. Such domains have in common the fact that information may be incomplete and/or uncertain, requiring approaches ...
Christian Theil Have, Riccardo Zese
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