Description Usage Arguments Author(s) References See Also
View source: R/bp.computeAlpha.R
This function computes the acceptance ratio of two changepoint configurations with networks in a changepoint birth or death move.
1 2  bp.computeAlpha(birth, lNew, kminus, Ekl, Estar, Ekr, yL, PxL, yR, PxR, y2, Px2,
D, delta2, q, smax, v0, gamma0, prior_ratio = 1)

birth 

lNew 
Number of edges in the new segment. 
kminus 
Minimal number of changepoints between the two compared models
(equal to 
Ekl 
Changepoint on the left of proposed changepoint. 
Estar 
Changepoint being inserted or deleted. 
Ekr 
Changepoint on the right of proposed changepoint. 
yL 
Response data (left). 
PxL 
Projection matrix (left). 
yR 
Response data (right). 
PxR 
Projection matrix (right). 
y2 
Response data (both). 
Px2 
Projection matrix (both). 
D 
Hyperparameters for the number of edges in each segment. 
delta2 
Hyperparameters for the empirical covariance (signaltonoise ratio). 
q 
Total number of nodes in the network. 
smax 
Maximum number of changepoints. 
v0 
Hyperparameter. 
gamma0 
Hyperparameter. 
prior_ratio 
Ratio of network structure priors. 
Sophie Lebre
For more information about the model, see:
Dondelinger et al. (2012), "Nonhomogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually timevarying structure", Machine Learning.
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