asggm {AdaptiveSparsity} | R Documentation |
implements a parameter-free adaptively sparse Gaussian graphical model.
## S3 method for class 'formula'
asggm(formula, data=list(), ...)
## Default S3 method:
asggm(x, iterations = 100000000, init = NULL, epsilon = 0.001, ...)
formula |
an object of class “formula” (or one that can be coerced to that class): a symbolic description of the model to be fitted.
See |
data |
an optional data frame, list or environment containing the variables in the model. |
x |
design matrix |
iterations |
number of iterations of the algorithm to run. |
init |
optional initialization, for instance, the cholesky of |
epsilon |
amount to add for numerical stability. |
... |
further arguments |
An effective approach to structure learning and parameter estimation for Gaussian graphical models is to impose a sparsity prior, such as a Laplace prior, on the entries of the precision matrix. We introduce a parameter-free method for estimating a precision matrix with sparsity that adapts to the data automatically, achieved by formulating a hierarchical Bayesian model of the precision matrix with a non-informative Jeffreys' hyperprior. We also naturally enforce the symmetry and positive-definiteness constraints on the precision matrix by parameterizing it with the Cholesky decomposition.
asggm
returns an object of class "asggm"
.
An object of class “asggm
” is a list containing at least the following components:
Kristen Zygmunt, Eleanor Wong, Tom Fletcher
Wong, Eleanor, Suyash Awate, and P. Thomas Fletcher. “Adaptive Sparsity in Gaussian Graphical Models.”In Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp. 311-319. 2013.
A = diag(3)
asggm(A)