generateBAPParameters: Randomly generate parameters (edge weights) for a given BAP
In cnowzohour/greedyBAPs: Greedy BAP Learning Using Penalised Maximum Likelihood Score
Description
Usage
Arguments
Value
Description
We consider the following linear recursive model:
X = B^t X + eps,
where (B)_i,j is the edge weight of edge i->j and corresponds to the (i,j)-entry
in the adjacency matrix of the graph, and eps has a N(0, Omega) distribution.
Usage
1
Arguments
bap
p-by-p adjacency matrix
Value
List containing:
B - p-by-p matrix of directed edge weights
Omega - p-by-p matrix of bidirected edge weights
cnowzohour/greedyBAPs documentation built on May 13, 2019, 1:36 a.m.
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Description Usage Arguments Value
Description
We consider the following linear recursive model: X = B^t X + eps, where (B)_i,j is the edge weight of edge i->j and corresponds to the (i,j)-entry in the adjacency matrix of the graph, and eps has a N(0, Omega) distribution.
Usage
1 |
Arguments
bap |
p-by-p adjacency matrix |
Value
List containing: B - p-by-p matrix of directed edge weights Omega - p-by-p matrix of bidirected edge weights
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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