Generates a value from the posterior distribution in the case where
there is a multivariate normal likelihood and a multivariate normal prior.
Argument Model:
y ~ Nn(X*beta, Sig)
beta ~ Np(mu, V)
1 | update_normal_normal(y, X, mu, Sig, V, Sig_inv, V_inv)
|
y |
vector of values at the likelihood level. |
X |
fixed design matrix in likelihood. |
mu |
prior mean vector. |
Sig, Sig_inv |
likelihood covariance/precision matrix. |
V, V_inv |
prior covariance/precision matrix. |
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