computePx: Compute projection matrix.
In EDISON: Network Reconstruction and Changepoint Detection
Description
Usage
Arguments
Value
Author(s)
References
See Also
Description
This function computes the projection matrix that is needed for calculation
of the likelihood.
Usage
1
computePx(len, x, delta2)
Arguments
len
Delimiting breakpoints.
x
The observations of x in the corresponding state.
delta2
Signal-to-noise ratio hyperparameter.
Value
The projection matrix.
Author(s)
Sophie Lebre
References
For more information about the hyperparameters and the
functional form of the likelihood, 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.
See Also
EDISON documentation built on May 2, 2019, 2:39 a.m.
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Description Usage Arguments Value Author(s) References See Also
Description
This function computes the projection matrix that is needed for calculation of the likelihood.
Usage
1 | computePx(len, x, delta2)
|
Arguments
len |
Delimiting breakpoints. |
x |
The observations of x in the corresponding state. |
delta2 |
Signal-to-noise ratio hyperparameter. |
Value
The projection matrix.
Author(s)
Sophie Lebre
References
For more information about the hyperparameters and the functional form of the likelihood, 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.
See Also
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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