unpenalized_likelihood_Q: Evaluate Negative Log Likelihood
In mlkrock/BasisGraphicalLasso: Basis Graphical Lasso

Description Usage Arguments Details Value

Evaluates the negative log likelihood without the normalizing constant.

1	unpenalized_neglikelihood_Q(Q, Phi_Dinv_Phi, Phi_Dinv_S_Dinv_Phi)

`Q`	Precision matrix.
`Phi_Dinv_Phi`	Inner product of basis matrices, Φ'D^{-1}Φ. Typically obtained from using `BGLBasisSetup.R` with D = τ^2 I.
`Phi_Dinv_S_Dinv_Phi`	Inner product of the basis matrices and data, Φ'D^{-1}SD^{-1}Φ. Typically obtained from using `BGLBasisSetup` with D = τ^2 I. This is where the data directly enters the algorithm. Note: do not compute sample covariance S explicitly. With `dat` as the n by m matrix with columns corresponding to realizations of the mean zero spatial field, we have S=XX'/m. So, setting D = I for simplicity, we can compute as Φ'SΦ as `tcrossprod(crossprod(Phi, dat))/m`.

This returns negative two times the log likelihood, ignoring anything that doesn't depend on Q or τ^2. Note that the evaluation only requires matrix computations in the number of the basis functions since Phi'Phi and Phi'SPhi are already computed.

-2*log likelihood, ignoring additive constants not depending on Q.

mlkrock/BasisGraphicalLasso documentation built on Dec. 21, 2021, 7:59 p.m.