def marginal_mu(self, mu):
"""Return Pr(mu)"""
# Don't have an independent source for this, so convert params to NIX and use that result.
mu_0 = self.m_0
kappa_0 = 1./self.V_0
nu_0 = 2*self.a_0
sigsqr_0 = 2*self.b_0/nu_0
return t_density(nu_0, mu_0, sigsqr_0/kappa_0, mu)
def pred(self, x):
"""Prior predictive. Pr(x)"""
return t_density(2.0*self.a_0, self.m_0, self.b_0*(1.0+self.V_0)/self.a_0, x)
def pred(self, x):
"""Prior predictive. Pr(x)"""
return t_density(self.nu_0, self.mu_0, (1.+self.kappa_0)*self.sigsqr_0/self.kappa_0, x)
def marginal_mu(self, mu):
return t_density(self.nu_0, self.mu_0, self.sigsqr_0/self.kappa_0, mu)
def pred(self, x):
"""Prior predictive. Pr(x)"""
# Careful. Use 1/beta/alpha to match Murphy, not wikipedia!
return t_density(2*self.alpha, self.mu, 1./self.beta/self.alpha, x)
