emission {BayesNetBP}

A ClusterTree Example of Emission Model

Description

A propagated ClusterTree object named emission. This model contains nine variables, indlucing three discrete: Filter State (Fs), Waste Type (W), Burning Regimen (B) and six continuous variables: Metals in Waste (Min), Metals Emission (Mout), Filter Efficiency (E), Dust Emission (D), CO2 Concentration in Emission (C), Light Penetrability (L).

Usage

data(emission)

Format

The data set contains a propagated ClusterTree object emission ready for evidence absorption and making queries.

References

Lauritzen, Steffen L., and Frank Jensen. Stable local computation with conditional Gaussian distributions. Statistics and Computing 11.2 (2001): 191-203.