Bayesian network modeling software

Active 3 years, 11 months ago. Viewed 37k times. Any suggestions would be appreciated. Improve this question. If the latter, from which? Back then, I used Inkscape and it was terrible - I had to hand edit the SVG to add in the greek letters, because it couldn't handle it back then maybe it still can't.

I don't recommend it for anyone. I'm going to give the tikz approach a shot. Show 3 more comments. Active Oldest Votes. There are a host of other benifits to using Tikz for such a project though Extensive documentation. The pgf manual holds your hand through making some similar diagrams.

And once you get your feet wet A huge library of examples is there to demonstrate how to produce a huge variety of graphics. And finally, the Tex stack exchange site is a good place to ask any questions about Tikz. They have some wizzes over there making some pretty fancy graphics check out their blog for some examples. Improve this answer. Add a comment. AI in Business. You may also like. Alpine Data Labs Aug 26, RapidMiner Review Mar 13, AgenaRisk Desktop is a model design and execution environment for Bayesian Networks that runs on Windows, Linux and Macintosh operating systems.

With AgenaRisk Desktop you can build any number of tailor-made models for any application domain or problem by exploiting statistical data and domain knowledge. AgenaRisk's technology is based on 30 years of research in risk, computer science, AI, Bayesian probability, statistics and smart data. Our technology and accompanying methodology has been published in top academic AI, machine learning, actuarial, decision science and cognitive science journals. AgenaRisk's Bayesian Network technology combines data and domain knowledge, in the form a causal network model of the problem.

BEST is a suite of enterprise-level stakeholder-specific modules for the design, administration and operational deployment of intelligent troubleshooting applications in complex technical areas.

It provides expressive yet easy-to-use tools to model and disseminate expert knowledge, machine learning capabilities to automatically improve models using in-service experience, and powerful optimization algorithms to generate troubleshooting strategies that minimize the cost of diagnosis.

BRICKS is a probabilistic relational modeling framework and technology platform based on an object-oriented extension of classical Bayesian networks. BRICKS also enhances inference efficiency by utilizing symmetries and leveraging advanced decompositions of probabilistic dependencies. Invented by Judea Pearl in the s at UCLA, Bayesian networks are a mathematical formalism that can simultaneously represent a multitude of probabilistic relationships between variables in a system.

The graph of a Bayesian network contains nodes representing variables and directed arcs that link the nodes. The arcs represent the relationships of the nodes.