The Hugin Tool for Learning Bayesian Networks

Madsen, Anders L.; Lang, Michael; Kjærulff, Uffe; Jensen, Frank

doi:10.1007/978-3-540-45062-7_49

Cited by 68 publications

(32 citation statements)

References 8 publications

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“…For instance, the necessary path condition (NPC) algorithm, which is an enhancement of the PC algorithm, allows the user to interactively decide on the directionality of the undirected links. The disadvantage of both PC and NPC algorithms is that their outcomes are structures and not conditional probability matrices (Madsen et al 2004). Friedman et al (1999) suggest an algorithm that limits the search space for learning a BN by restricting the parents of each variable to belong to a small subset of candidates.…”

Section: Bayesian Networkmentioning

confidence: 99%

A new heuristic for learning Bayesian networks from limited datasets: a real-time recommendation system application with RFID systems in grocery stores

Cinicioglu

Shenoy

2012

Ann Oper Res

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Bayesian networks (BNs) are a useful tool for applications where dynamic decision-making is involved. However, it is not easy to learn the structure and conditional probability tables of BNs from small datasets. There are many algorithms and heuristics for learning BNs from sparse datasets, but most of these are not concerned with the quality of the learned network in the context of a specific application. In this research, we develop a new heuristic on how to build BNs from sparse datasets in the context of its performance in a real-time recommendation system. This new heuristic is demonstrated using a market basket dataset and a real-time recommendation model where all items in the grocery store are RFID tagged and the carts are equipped with an RFID scanner. With this recommendation model, retailers are able to do real-time recommendations to customers based on the products placed in cart during a shopping event.

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Section: Bayesian Networkmentioning

confidence: 99%

A new heuristic for learning Bayesian networks from limited datasets: a real-time recommendation system application with RFID systems in grocery stores

Cinicioglu

Shenoy

2012

Ann Oper Res

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“…To improve performance, fractional updating is only performed for π ij when P (π ij ) > 0 (an update would leave θ * ijk and α ij unchanged when P (π ij ) = 0)), see [10]. Both fractional updating and Online EM perform no update when α ij = 0.…”

Section: Parameter Learning Algorithmsmentioning

confidence: 99%

“…A similar gradient descent approach is described in [5]. [10] describes how the approach of [16] referred to as sequential learning has been implemented in the HUGIN tool. The online EM algorithm [2] is a stochastic gradient method that is faster than other gradient methods such as [19] which involves a difficult task of determining the step size between iterations.…”

Section: Introductionmentioning

confidence: 99%

Parameter Learning Algorithms for Continuous Model Improvement Using Operational Data

Madsen

Jeppesen

Jensen

et al. 2017

Lecture Notes in Computer Science

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“…The algorithms were implemented using HUGIN software version 8.0 [17,18] and MPI. The HUGIN software does not have any special features necessary for implementing the ideas presented in this paper.…”

Section: Fyrkatmentioning

confidence: 99%

A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs

Madsen

Jensen

Salmerón

et al. 2014

Probabilistic Graphical Models

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Abstract. The framework of Bayesian networks is a widely popular formalism for performing belief update under uncertainty. Structure restricted Bayesian network models such as the Naive Bayes Model and Tree-Augmented Naive Bayes (TAN) Model have shown impressive performance for solving classification tasks. However, if the number of variables or the amount of data is large, then learning a TAN model from data can be a time consuming task. In this paper, we introduce a new method for parallel learning of a TAN model from large data sets. The method is based on computing the mutual information scores between pairs of variables given the class variable in parallel. The computations are organised in parallel using balanced incomplete block designs. The results of a preliminary empirical evaluation of the proposed method on large data sets show that a significant performance improvement is possible through parallelisation using the method presented in this paper.

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The Hugin Tool for Learning Bayesian Networks

Cited by 68 publications

References 8 publications

A new heuristic for learning Bayesian networks from limited datasets: a real-time recommendation system application with RFID systems in grocery stores

A new heuristic for learning Bayesian networks from limited datasets: a real-time recommendation system application with RFID systems in grocery stores

Parameter Learning Algorithms for Continuous Model Improvement Using Operational Data

A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs

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