Nonlinear Deterministic Relationships in Bayesian Networks

In a Bayesian network with continuous variables containing a variable(s) that is a conditionally deterministic function of its continuous parents, the joint density function for the variables in the network does not exist. Conditional linear Gaussian distributions can handle such cases when the deterministic function is linear and the continuous variables have a multi-variate normal distribution. In this paper, operations required for performing inference with nonlinear conditionally deterministic variables are developed. We perform inference in networks with nonlinear deterministic variables and non-Gaussian continuous variables by using piecewise linear approximations to nonlinear functions and modeling probability distributions with mixtures of truncated exponentials (MTE) potentials. Key Words: Bayesian networks, MTE potentials, inference, CLG models ∗ Comments and suggestions for improvement are welcome and will be gratefully appreciated. 

• Type: Conference
• Year: 2005
• Where: ECSQARU
• Authors: Barry R. Cobb, Prakash P. Shenoy
• Keywords: Continuous Variables, Deterministic Function, Deterministic Variables, ECSQARU 2005