Software packages for graphical models/Bayesian networks. Graphical models (GMs) are a way to represent conditional independence assumptions by using graphs. Speciﬁcally, nodes represent random variables and lack of edges represent conditional independencies. The graph is a useful visual representation of complex stochastic systems. The graphical structure is also the basis of eﬃcient inference algorithms.There are many diﬀerent kinds of graphical models, but the two most popular ones are based on directed acylic graphs (also called “Bayesian networks”) and on undirected graphs (also called “Markov random ﬁelds”). In this article, we review some of the more popular and/or recent software packages for dealing with graphical models. A more extensive comparison can be found at http://www.cs.ubc.ca/ murphyk/Software/bnsoft.html.
Keywords for this software
References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Venkat, Ibrahim; De Wilde, Philippe: Robust gait recognition by learning and exploiting sub-gait characteristics (2011)
- Vihola, Matti: Grapham: graphical models with adaptive random walk Metropolis algorithms (2010)
- Blecic, Ivan; Cecchini, Arnaldo; Trunfio, Giuseppe A.: A decision support tool coupling a causal model and a multi-objective genetic algorithm (2007)