Bayesian updating in causal probabilistic networks by local computations christian dating 3rd date

A broad background of theory and methods have been developed for the case in which all the variables are discrete.

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These models are based on the CG and MTE distributions.

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In such cases, usually the continuous variables are discretized and therefore all the existing methods for discrete variables can be applied, but the price to pay is that the obtained model is just an approximation.

In this chapter we study two frameworks where continuous and discrete variables can be handled simultaneously without using discretization.

We describe how both single and multi dimensional partial derivatives of the evidence may easily be calculated from a junction tree in LARP equilibrium.

We show that the simplicity of the calculations stems from the nature of LARP.

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and introduces Probanet—a development environment for CPNs.

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