What is it about?
The paper is about solving Bayesian networks containing discrete and continuous variables with deterministic conditionals using mixtures of polynomials and mixtures of truncated exponentials. We take a small example from stochastic PERT (project evaluation and review technique) containing continuous variables with non-Gaussian conditionals and solve it. Challenges in the solution process are addressed.
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Why is it important?
One of the challenges using mixtures of polynomials and mixtures of truncated exponentials is the exponential growth of number of mixtures. This is addressed by re-approximating the mixture with fewer components.
Perspectives
We provide an "exact" solution to a typical problem in project management. By "exact," we mean using non-Monte Carlo techniques.
Distinguished Professor Emeritus Prakash Pundalik Shenoy
University of Kansas School of Business
Read the Original
This page is a summary of: Practical Aspects of Solving Hybrid Bayesian Networks Containing Deterministic Conditionals, International Journal of Intelligent Systems, December 2014, Hindawi Publishing Corporation,
DOI: 10.1002/int.21700.
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