BNFinder: exact and efficient method for learning Bayesian networks

BNFinder: exact and efficient method for learning Bayesian networks

MOTIVATION Bayesian methods are widely used in many different areas of research. Recently, it has become a very popular tool for biological network reconstruction, due to its ability to handle noisy data. Even though there are many software packages allowing for Bayesian network reconstruction, only few of them are freely available to researchers. Moreover, they usually require at least basic p...

Fast Parallel Bayesian Networks Reconstruction with BNFinder

Bayesian networks are probabilistic graphical models widely used to infer interactions between biological entities such as genes or proteins. In general, learning Bayesian networks from experimental data is NP-hard, leading to widespread use of heuristic search methods giving suboptimal results. However, in a number of important special cases, it is possible to find the optimal network in polyn...

An Efficient Methodology for Learning Bayesian Networks

Exact Bayesian Learning of Ancestor Relations in Bayesian Networks

Ancestor relations in Bayesian networks (BNs) encode long-range causal relations among random variables. In this paper, we develop dynamic programming (DP) algorithms to compute the exact posterior probabilities of ancestor relations in Bayesian networks. Previous algorithm by Parviainen and Koivisto (2011) evaluates all possible ancestor relations in time O(n3) and space O(3). However, their a...

A Parallel Algorithm for Exact Structure Learning of Bayesian Networks

Given n random variables and a set of m observations of each of the n variables, the Bayesian network (BN) structure learning problem is to infer a directed acyclic graph on the n variables such that the implied joint probability distribution best explains the set of observations. In this paper, we present a parallel algorithm for exact BN structure learning that is work-optimal and communicati...