Open AccessPosted Content
A Birth and Death Process for Bayesian Network Structure Inference
Dale Jennings,J. N. Corcoran +1 more
TL;DR: In this paper, the authors model the appearance and disappearance of edges as a birth and death process and compare their approach to the popular Metropolis-Hastings search strategy, and give empirical evidence that the birth-and-death process has superior mixing properties.
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Abstract: Bayesian networks (BNs) are graphical models that are useful for representing high-dimensional probability distributions. There has been a great deal of interest in recent years in the NP-hard problem of learning the structure of a BN from observed data. Typically, one assigns a score to various structures and the search becomes an optimization problem that can be approached with either deterministic or stochastic methods. In this paper, we walk through the space of graphs by modeling the appearance and disappearance of edges as a birth and death process and compare our novel approach to the popular Metropolis-Hastings search strategy. We give empirical evidence that the birth and death process has superior mixing properties.
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Citations
•Posted Content
Efficient Sampling and Structure Learning of Bayesian Networks
TL;DR: A novel hybrid method which reduces the complexity of MCMC approaches to that of a constraint-based method and offers markedly superior performance to alternatives, particularly because DAGs can also be sampled from the posterior distribution, enabling full Bayesian model averaging for much larger Bayesian networks.
70
•Posted Content
Efficient Structure Learning and Sampling of Bayesian Networks.
Jack Kuipers,Polina Suter,Giusi Moffa +2 more
- 21 Mar 2018
TL;DR: This work synthesises constraint based methods that perform conditional independence tests to exclude edges and score and search approaches which explore the DAG space with greedy or MCMC schemes in a novel hybrid method which reduces the complexity of MCMC approaches to that of a constraint based method.
11
References
Using Bayesian networks to analyze expression data
TL;DR: A new framework for discovering interactions between genes based on multiple expression measurements is proposed and a method for recovering gene interactions from microarray data is described using tools for learning Bayesian networks.
3.7K
Bayesian Graphical Models for Discrete Data
David Madigan,Jeremy C. York +1 more
TL;DR: In this paper, the authors introduce the composition of chaines de Markov-Monte Carlo, a methode de Monte Carlo permettant de moyenner sur les modeles retenus.
1.2K