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These value functions maintain the independence structure implicit in the original statements.
A graphical model is a probabilistic model for which a graph denotes the conditional independence structure between random variables.
Graphs provide an excellent framework for interrogating symmetric models of measurement random variables and discovering their implied conditional independence structure.
We also show how the topology can be exploited to tease out the interesting conditional independence structure of functions of random variables associated with the underlying event tree.
In this paper we formalise and illustrate with two practical examples how these probabilistic propagation algorithms can be applied to high dimensional processes whose conditional independence structure, as well as their underlying distributions, are augmented through the passage of time.
It has been shown that junction tree algorithms can provide a quick and efficient method for propagating probabilities in complex multivariate problems when they can be described by a fixed conditional independence structure.
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A framework for log-linear models with context specific independence structures, i.e. conditional independencies holding only for specific values of the conditioning variables is introduced.
In our modelling we assumed a working independence correlation structure, not an unstructured correlation structure.
On Pakistani independence, the structure of state-Raj versus the citizen-native did not change.
Also, the postulated independence of structure and activity is of little significance to the overall assessment.
Computation of predicted probability of distinctiveness employs joint probability of c, s and of c, d assuming independence of structure and activity.
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