def log_prior(self):
"""
Compute the log likelihood of a set of SBM parameters
:param x: (m,p,v) tuple
:return:
"""
from scipy.stats import dirichlet
from graphistician.internals.utils import normal_inverse_wishart_log_prob
lp = 0
# Get the log probability of the block probabilities
lp += dirichlet(self.pi).logpdf(self.m)
# Get the prior probability of the Gaussian parameters under NIW prior
for c1 in xrange(self.C):
for c2 in xrange(self.C):
lp += normal_inverse_wishart_log_prob(self._gaussians[c1][c2])
if self.special_case_self_conns:
lp += normal_inverse_wishart_log_prob(self._self_gaussian)
# Get the probability of the block assignments
lp += (np.log(self.m)[self.c]).sum()
return lp
def log_prior(self):
from graphistician.internals.utils import normal_inverse_wishart_log_prob
lp = 0
lp += normal_inverse_wishart_log_prob(self._gaussian)
lp += normal_inverse_wishart_log_prob(self._self_gaussian)
return lp
def log_prior(self):
"""
Compute the log likelihood of a set of SBM parameters
:param x: (m,p,v) tuple
:return:
"""
from scipy.stats import dirichlet
from graphistician.internals.utils import normal_inverse_wishart_log_prob
lp = 0
# Get the log probability of the block probabilities
lp += dirichlet(self.pi).logpdf(self.m)
# Get the prior probability of the Gaussian parameters under NIW prior
for c1 in xrange(self.C):
for c2 in xrange(self.C):
lp += normal_inverse_wishart_log_prob(self._gaussians[c1][c2])
if self.special_case_self_conns:
lp += normal_inverse_wishart_log_prob(self._self_gaussian)
# Get the probability of the block assignments
lp += (np.log(self.m)[self.c]).sum()
return lp
def log_prior(self):
from graphistician.internals.utils import normal_inverse_wishart_log_prob
lp = 0
lp += normal_inverse_wishart_log_prob(self._gaussian)
lp += normal_inverse_wishart_log_prob(self._self_gaussian)
return lp
def log_prior(self):
"""
Compute the prior probability of F, mu0, and lmbda
"""
from graphistician.internals.utils import \
normal_inverse_wishart_log_prob, \
inverse_wishart_log_prob
lp = 0
# Log prior of F under spherical Gaussian prior
from scipy.stats import norm, invgamma
lp += invgamma.logpdf(self.eta, 1, 1)
lp += norm.logpdf(self.b, 0, 1)
lp += norm.logpdf(self.L, 0, 1).sum()
lp += inverse_wishart_log_prob(self.cov)
lp += normal_inverse_wishart_log_prob(self._self_gaussian)
# Log prior of mu_0 and mu_self
return lp
def log_prior(self):
"""
Compute the prior probability of F, mu0, and lmbda
"""
from graphistician.internals.utils import \
normal_inverse_wishart_log_prob, \
inverse_wishart_log_prob
lp = 0
# Log prior of F under spherical Gaussian prior
from scipy.stats import norm, invgamma
lp += invgamma.logpdf(self.eta, 1, 1)
lp += norm.logpdf(self.b, 0, 1)
lp += norm.logpdf(self.L, 0, 1).sum()
lp += inverse_wishart_log_prob(self.cov)
lp += normal_inverse_wishart_log_prob(self._self_gaussian)
# Log prior of mu_0 and mu_self
return lp
