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 (signal-to-noise 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), "Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure", Machine Learning.
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