ExpHyperMove: Makes an exponential hyperparameter move.

In EDISON: Network Reconstruction and Changepoint Detection

Description

This function tries to make a level-1 or level-2 hyperparameter move for the exponential prior

Usage

ExpHyperMove(network.info, node.sharing, GLOBvar, hyper.proposals)

Arguments

network.info	The network information collected by CollectNetworkInfo.
node.sharing	The type of information sharing among nodes: 'soft' or 'hard'.
GLOBvar	Collection of global variables of the MCMC.
hyper.proposals	Proposal width of the hyperparameter move.

Value

Returns a list with elements:

move.made	1 if a level-1 hyperparameter move has been made, 0 otherwise.
network.info	Network information with updated hyperparameters if the move was accepted.
accept	Whether a level-1 hyperparameter move has been accepted or not.
move.made.k	1 if a level-2 hyperparameter move has been made, 0 otherwise.
accept.k	Whether a level-2 hyperparameter move has been accepted or not.
move	Type of move: 2 for a level-1 hyperparameter move, 3 for a level-2 hyperparameter move.

Author(s)

Frank Dondelinger

References

For information about the exponential information sharing prior, see:

Husmeier et al. (2010), "Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks", NIPS.

Dondelinger et al. (2012), "Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure", Machine Learning.

See Also

ExpHyperRatioTarget

EDISON documentation built on May 2, 2019, 2:39 a.m.