Add to ORKGAbstract. We discuss how to learn non-recursive directed probabilistic logical models from relational data. This problem has been tackled before by upgrading the structure-search algorithm initially proposed for Bayesian networks. In this paper we propose to upgrade another algorithm, namely ordering-search, since for Bayesian networks this was found to work better than structure-search. We experimentally compare the two upgraded algorithms on two relational domains. We conclude that there is no significant difference between the two algorithms in terms of quality of the learnt models while ordering-search is significantly faster
International audienceProbabilistic relational models (PRMs) extend Bayesian networks (BNs) to a rel...
Various representations and inference methods have been proposed for lifted probabilistic inference ...
Statistical Relational Learning is a new branch of machine learning that aims to model a joint distr...
We discuss how to learn non-recursive directed probabilistic logical models from relational data. Th...
Abstract We discuss how to learn non-recursive directed probabilistic logical models from relational...
There is an increasing interest in upgrading Bayesian networks to the relational case, resulting in ...
Abstract. Recently, there has been an increasing interest in directed probabilistic logical models a...
Recently, there has been an increasing interest in probabilistic logical models and a variety of suc...
Data that has a complex relational structure and in which observations are noisy or partially missin...
Data that has a complex relational structure and in which observations are noisy or partially missin...
Integrating the expressive power of first-order logic with the probabilistic reasoning power of Baye...
Many machine learning applications that involve relational databases incorporate first-order logic a...
Many machine learning applications that involve relational databases incorporate first-order logic a...
Several models combining Bayesian networks with logic exist. The two most developed models are Proba...
Tractable Bayesian network learning’s goal is to learn Bayesian networks (BNs) where inference is gu...
International audienceProbabilistic relational models (PRMs) extend Bayesian networks (BNs) to a rel...
Various representations and inference methods have been proposed for lifted probabilistic inference ...
Statistical Relational Learning is a new branch of machine learning that aims to model a joint distr...
We discuss how to learn non-recursive directed probabilistic logical models from relational data. Th...
Abstract We discuss how to learn non-recursive directed probabilistic logical models from relational...
There is an increasing interest in upgrading Bayesian networks to the relational case, resulting in ...
Abstract. Recently, there has been an increasing interest in directed probabilistic logical models a...
Recently, there has been an increasing interest in probabilistic logical models and a variety of suc...
Data that has a complex relational structure and in which observations are noisy or partially missin...
Data that has a complex relational structure and in which observations are noisy or partially missin...
Integrating the expressive power of first-order logic with the probabilistic reasoning power of Baye...
Many machine learning applications that involve relational databases incorporate first-order logic a...
Many machine learning applications that involve relational databases incorporate first-order logic a...
Several models combining Bayesian networks with logic exist. The two most developed models are Proba...
Tractable Bayesian network learning’s goal is to learn Bayesian networks (BNs) where inference is gu...
International audienceProbabilistic relational models (PRMs) extend Bayesian networks (BNs) to a rel...
Various representations and inference methods have been proposed for lifted probabilistic inference ...
Statistical Relational Learning is a new branch of machine learning that aims to model a joint distr...