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Furthermore, the one-dimensional marginal probability density function transport equation for the hole radius is derived and the conditional edge-flame velocity is studied for both expanding and collapsing holes.
We also tested how decreasing the conditional edge-addition probability q affects λ g and.
(4) Calculate the percentage variable b is in the neighborhood of a in each bootstrap sample b* as μ b a. (5) Define the neighborhood of variable a as: n e a * = { b | μ b a ≥ π c u t } (6) Define the set of conditional relationships (the edge set) E as: E = { (a, b ) | a ∈ n e b * ∨ b ∈ n e a * }.
Bayesian Network can be used to model a set of random variables (nodes) and their conditional dependencies (directed edges) [ 11].
Except for this class of conditional contacts, all edges in the network are undirected, which indicates that transmission can take place in either direction between the 2 persons who are in contact with each other.
The remaining connections were "rewired" at random, using three criteria, R, F and B. Criterion R was a uniformly random permutation of the edge ends, conditional on obtaining a "correct network" – no self-loops or duplicated edges.
Their nodes represent random variables (discrete or continuous) and the edges represent conditional dependencies, forming a directed acyclic graph.
The edges represent conditional dependencies, each node being associated with a probability function that takes as input a particular set of values for the node's parent variables and gives the probability of the variable represented by the node.
The nodes represent random variables, while the edges indicate conditional dependence relations.
Edges represent conditional dependencies; nodes that are not connected represent variables that are conditionally independent.
Nodes correspond to the random variables in V and edges encode conditional dependencies over V.
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