Approximate Probabilistic Inference with Bounded Error for Hybrid Probabilistic Logic Programming

A new probabilistic constraint logic programming language based on a generalised distribution semantics

Michels, S.
Hommersom, A.
Lucas, P.J.F.
Velikova, M.

January 2015

Abstract Probabilistic logics combine the expressive power of logic with the ability to reason with ...

MCINTYRE: A Monte Carlo Algorithm for Probabilistic Logic Programming

F. Riguzzi

January 2011

Probabilistic Logic Programming is receiving an increasing attention for its ability to model domain...

MCINTYRE: A Monte Carlo System for Probabilistic Logic Programming

F. Riguzzi

January 2013

Probabilistic Logic Programming is receiving an increasing attention for its ability to model domain...

Hybrid Probabilistic Logics: Theoretical Aspects, Algorithms and Experiments

Michels, S.

January 2016

Contains fulltext : 157121.pdf (publisher's version ) (Open Access)Steffen Michels...

Approximate inference techniques with expectation constraints.

Heskes, T.M.
Opper, M.
Wiegerinck, W.A.J.J.
Winther, O.
Zoeter, O.R.

January 2005

Contains fulltext : 100937.pdf (preprint version ) (Open Access

Toward Computing Conflict-Based Diagnoses in Probabilistic Logic Programming

Hommersom, A.J.
Bueno, M.L.P.

January 2016

Contains fulltext : 159599.pdf (preprint version ) (Open Access)nul

Knowledge Compilation with Continuous Random Variables and its Application in Hybrid Probabilistic Logic Programming

Zuidberg Dos Martires, Pedro Miguel
Dries, Anton
De Raedt, Luc

July 2018

In probabilistic reasoning, the traditionally discrete domain has been elevated to the hybrid domain...

The magic of logical inference in probabilistic programming

Gutmann, Bernd
Thon, Ingo
Kimmig, Angelika
Bruynooghe, Maurice
De Raedt, Luc

July 2011

Today, many different probabilistic programming languages exist and even more inference mechanisms f...

Bridging the gap between theory and practice of approximate Bayesian inference

Kwisthout, J.H.P.
Rooij, I.J.E.I. van

January 2013

Contains fulltext : 111296.pdf (author's version ) (Open Access)7 p

Conditioning in Probabilistic Programming

Olmedo, Federico
Gretz, Friedrich
Jansen, Nils
Kaminski, Benjamin Lucien
Katoen, Joost-Pieter
Mciver, Annabelle

January 2018

Contains fulltext : 182251.pdf (preprint version ) (Open Access) ...

Approximate inference and constrained optimization

Heskes, T.
Albers, C.A.
Kappen, H.J.

January 2003

Contains fulltext : 100940.pdf (preprint version ) (Open Access)UAI'0

Hybrid Probabilistic Logic Programming

Nitti, Davide

August 2016

An important issue in artificial intelligence and many other fields is modeling the domain of intere...

Approximate inference with expectation constraints

Heskes, T.M.
Opper, M.
Wiegerinck, W.A.J.J.
Winther, O.
Zoeter, O.R.

January 2005

Contains fulltext : 33238.pdf (author's version ) (Open Access

Under consideration for publication in Theory and Practice of Logic Programming 1 The Magic of Logical Inference in Probabilistic Programming

Bernd Gutmann
Ingo Thon
Angelika Kimmig
Maurice Bruynooghe
Luc De Raedt

January 2003

Today, many different probabilistic programming languages exist and even more inference mechanisms f...

Hybrid Probabilistic Logic Programs as Residuated Logic Programs

Carlos Viegas Damasio
Luis Moniz Pereira

January 2001

In this paper we show the embedding of Hybrid Probabilistic Logic Programs into the rather general f...

A new probabilistic constraint logic programming language based on a generalised distribution semantics

Michels, S.
Hommersom, A.
Lucas, P.J.F.
Velikova, M.

January 2015

Abstract Probabilistic logics combine the expressive power of logic with the ability to reason with ...

MCINTYRE: A Monte Carlo Algorithm for Probabilistic Logic Programming

F. Riguzzi

January 2011

Probabilistic Logic Programming is receiving an increasing attention for its ability to model domain...

MCINTYRE: A Monte Carlo System for Probabilistic Logic Programming

F. Riguzzi

January 2013

Probabilistic Logic Programming is receiving an increasing attention for its ability to model domain...

Hybrid Probabilistic Logics: Theoretical Aspects, Algorithms and Experiments

Michels, S.

January 2016

Contains fulltext : 157121.pdf (publisher's version ) (Open Access)Steffen Michels...

Approximate inference techniques with expectation constraints.

Heskes, T.M.
Opper, M.
Wiegerinck, W.A.J.J.
Winther, O.
Zoeter, O.R.

January 2005

Contains fulltext : 100937.pdf (preprint version ) (Open Access

Toward Computing Conflict-Based Diagnoses in Probabilistic Logic Programming

Hommersom, A.J.
Bueno, M.L.P.

January 2016

Contains fulltext : 159599.pdf (preprint version ) (Open Access)nul

Knowledge Compilation with Continuous Random Variables and its Application in Hybrid Probabilistic Logic Programming

Zuidberg Dos Martires, Pedro Miguel
Dries, Anton
De Raedt, Luc

July 2018

In probabilistic reasoning, the traditionally discrete domain has been elevated to the hybrid domain...

The magic of logical inference in probabilistic programming

Gutmann, Bernd
Thon, Ingo
Kimmig, Angelika
Bruynooghe, Maurice
De Raedt, Luc

July 2011

Today, many different probabilistic programming languages exist and even more inference mechanisms f...

Bridging the gap between theory and practice of approximate Bayesian inference

Kwisthout, J.H.P.
Rooij, I.J.E.I. van

January 2013

Contains fulltext : 111296.pdf (author's version ) (Open Access)7 p

Conditioning in Probabilistic Programming

Olmedo, Federico
Gretz, Friedrich
Jansen, Nils
Kaminski, Benjamin Lucien
Katoen, Joost-Pieter
Mciver, Annabelle

January 2018

Contains fulltext : 182251.pdf (preprint version ) (Open Access) ...

Approximate inference and constrained optimization

Heskes, T.
Albers, C.A.
Kappen, H.J.

January 2003

Contains fulltext : 100940.pdf (preprint version ) (Open Access)UAI'0

Hybrid Probabilistic Logic Programming

Nitti, Davide

August 2016

An important issue in artificial intelligence and many other fields is modeling the domain of intere...

Approximate inference with expectation constraints

Heskes, T.M.
Opper, M.
Wiegerinck, W.A.J.J.
Winther, O.
Zoeter, O.R.

January 2005

Contains fulltext : 33238.pdf (author's version ) (Open Access

Under consideration for publication in Theory and Practice of Logic Programming 1 The Magic of Logical Inference in Probabilistic Programming

Bernd Gutmann
Ingo Thon
Angelika Kimmig
Maurice Bruynooghe
Luc De Raedt

January 2003

Today, many different probabilistic programming languages exist and even more inference mechanisms f...

Hybrid Probabilistic Logic Programs as Residuated Logic Programs

Carlos Viegas Damasio
Luis Moniz Pereira

January 2001

In this paper we show the embedding of Hybrid Probabilistic Logic Programs into the rather general f...

A new probabilistic constraint logic programming language based on a generalised distribution semantics

Michels, S.
Hommersom, A.
Lucas, P.J.F.
Velikova, M.

January 2015

Abstract Probabilistic logics combine the expressive power of logic with the ability to reason with ...

MCINTYRE: A Monte Carlo Algorithm for Probabilistic Logic Programming

F. Riguzzi

January 2011

Probabilistic Logic Programming is receiving an increasing attention for its ability to model domain...

MCINTYRE: A Monte Carlo System for Probabilistic Logic Programming

F. Riguzzi

January 2013

Probabilistic Logic Programming is receiving an increasing attention for its ability to model domain...

Approximate Probabilistic Inference with Bounded Error for Hybrid Probabilistic Logic Programming

Abstract

Extracted data

Approximate Probabilistic Inference with Bounded Error for Hybrid Probabilistic Logic Programming

Abstract

Extracted data

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