James Cussens

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University of York ∙ University of Bristol

A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR Relations

A Bayesian network is a probabilistic graphical model that consists of a...
11/03/2020 ∙ by Charupriya Sharma, et al. ∙ 0 ∙ share

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Learning All Credible Bayesian Network Structures for Model Averaging

A Bayesian network is a widely used probabilistic graphical model with a...
08/27/2020 ∙ by Zhenyu A. Liao, et al. ∙ 20 ∙ share

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Kernel-based Approach to Handle Mixed Data for Inferring Causal Graphs

Causal learning is a beneficial approach to analyze the cause and effect...
10/07/2019 ∙ by Teny Handhayani, et al. ∙ 0 ∙ share

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On Pruning for Score-Based Bayesian Network Structure Learning

Many algorithms for score-based Bayesian network structure learning (BNS...
05/23/2019 ∙ by Alvaro H. C. Correia, et al. ∙ 0 ∙ share

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Online Causal Structure Learning in the Presence of Latent Variables

We present two online causal structure learning algorithms which can tra...
04/30/2019 ∙ by Durdane Kocacoban, et al. ∙ 0 ∙ share

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Finding All Bayesian Network Structures within a Factor of Optimal

A Bayesian network is a widely used probabilistic graphical model with a...
11/12/2018 ∙ by Zhenyu A. Liao, et al. ∙ 0 ∙ share

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Finding Minimal Cost Herbrand Models with Branch-Cut-and-Price

Given (1) a set of clauses T in some first-order language L and (2) a c...
08/14/2018 ∙ by James Cussens, et al. ∙ 0 ∙ share

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Exact Estimation of Multiple Directed Acyclic Graphs

This paper considers the problem of estimating the structure of multiple...
04/04/2014 ∙ by Chris J. Oates, et al. ∙ 0 ∙ share

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Advances in Bayesian Network Learning using Integer Programming

We consider the problem of learning Bayesian networks (BNs) from complet...
09/26/2013 ∙ by Mark Bartlett, et al. ∙ 0 ∙ share

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Loglinear models for first-order probabilistic reasoning

Recent work on loglinear models in probabilistic constraint logic progra...
01/23/2013 ∙ by James Cussens, et al. ∙ 0 ∙ share

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Stochastic Logic Programs: Sampling, Inference and Applications

Algorithms for exact and approximate inference in stochastic logic progr...
01/16/2013 ∙ by James Cussens, et al. ∙ 0 ∙ share

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Markov Chain Monte Carlo using Tree-Based Priors on Model Structure

We present a general framework for defining priors on model structure an...
01/10/2013 ∙ by Nicos Angelopoulos, et al. ∙ 0 ∙ share

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CLP(BN): Constraint Logic Programming for Probabilistic Knowledge

We present CLP(BN), a novel approach that aims at expressing Bayesian ne...
10/19/2012 ∙ by Vitor Santos Costa, et al. ∙ 0 ∙ share

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Bayesian network learning by compiling to weighted MAX-SAT

The problem of learning discrete Bayesian networks from data is encoded ...
06/13/2012 ∙ by James Cussens, et al. ∙ 0 ∙ share

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Bayesian network learning with cutting planes

The problem of learning the structure of Bayesian networks from complete...
02/14/2012 ∙ by James Cussens, et al. ∙ 0 ∙ share

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James Cussens

Featured Co-authors

Chris J. Oates

Jim Q. Smith

David Page

Sach Mukherjee

Vitor Santos Costa

Cassio P. de Campos

Peter van Beek

Alvaro H. C. Correia

Nicos Angelopoulos

Zhenyu A. Liao

Charupriya Sharma

A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR Relations

Learning All Credible Bayesian Network Structures for Model Averaging

Kernel-based Approach to Handle Mixed Data for Inferring Causal Graphs

On Pruning for Score-Based Bayesian Network Structure Learning

Online Causal Structure Learning in the Presence of Latent Variables

Finding All Bayesian Network Structures within a Factor of Optimal

Finding Minimal Cost Herbrand Models with Branch-Cut-and-Price

Exact Estimation of Multiple Directed Acyclic Graphs

Advances in Bayesian Network Learning using Integer Programming

Loglinear models for first-order probabilistic reasoning

Stochastic Logic Programs: Sampling, Inference and Applications

Markov Chain Monte Carlo using Tree-Based Priors on Model Structure

CLP(BN): Constraint Logic Programming for Probabilistic Knowledge

Bayesian network learning by compiling to weighted MAX-SAT

Bayesian network learning with cutting planes