def inverse_wishart_log_prob(gaussian):
"""
Compute the log probability under a IW prior for a
GaussianFixedMean object
:param gaussian:
:return:
"""
from scipy.stats import invwishart
lp = 0
lp += invwishart.logpdf(gaussian.sigma,
gaussian.nu_0,
gaussian.lmbda_0)
return lp
def logpdf(self, X):
"""
Log PDF for Inverse Wishart prior
Parameters
----------
X : float
Covariance matrix for which the prior is being formed over
Returns
----------
- log(p(X))
"""
return invwishart.logpdf(X, df=self.v, scale=self.Psi)
def normal_inverse_wishart_log_prob(gaussian):
"""
Compute the log probability under a NIW prior for a Gaussian object
:param gaussian:
:return:
"""
from scipy.stats import invwishart, multivariate_normal
lp = 0
lp += invwishart.logpdf(gaussian.sigma,
gaussian.nu_0,
gaussian.sigma_0)
# Compute log N(mu_0 | mu_0, Sigma / kappa_0)
lp += multivariate_normal.logpdf(gaussian.mu,
gaussian.mu_0,
gaussian.sigma / gaussian.kappa_0)
return lp
def logpdf(self,X):
return invwishart.logpdf(X, df=self.v, scale=self.Psi)
