posterior: Posterior probability calculation.

In ddepn: Dynamic Deterministic Effects Propagation Networks: Infer signalling networks for timecourse RPPA data.

Description

Posterior probability calculation. Usually called internally.

Usage

posterior(phi, L, lambda=NULL, B=NULL, Z=NULL, gam=NULL, it=NULL, 
		K=NULL, priortype="laplaceinhib")
pri(i,gam,N)
pgs(phi,gam,K=0.8,it=500)

Arguments

phi	The candidate network.
L	The likelihood computed by likl.
lambda	Laplace prior hyperparameter describing the prior influence strength.
B	Laplace prior probability matrix.
Z	Laplace prior normalisation factor for the prior. (Not used at the moment.)
gam	Scale-free prior degree distribution coefficient: P(k) ~ k^gam
N	Number of nodes
K	Scale-free prior scaling factor/Strength
it	Scale-free prior number of iterations for prior sampling.
priortype	Character. One of uniform, laplaceinhib, laplace or scalefree for use of the respective prior type.

Details

Computes the posterior density depending on priortype: uniform uses uniform prior, laplaceinhib and laplace use prior parameters lambda, gam, B and Z, and scalefree uses gam and K as prior parameters. See prior for a description of the prior models.

Value

A double containing the posterior density.

Note

TODO

Author(s)

Christian Bender

References

Laplace prior
Froehlich et. al. 2007, Large scale statistical inference of signaling pathways from RNAi and microarray data.

Scale free prior
Kamimura and Shimodaira, A Scale-free Prior over Graph Structures for Bayesian Inference of Gene Networks

See Also

TODO

Examples

## TODO

ddepn documentation built on May 2, 2019, 4:42 p.m.