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)