def logtildegamma(G, Z, X, supp, w, G_mu, G_Sigma, eta):
prob = gv.sigmoid(w + gv.logit(Z))
llh = np.sum(supp * X * clog(prob)) + np.sum(supp *
(1 - X) * clog(1 - prob))
prior = logpdf(G, G_mu, G_Sigma)
#print llh, prior
return llh + prior
def logtildegamma(G, Z, X, supp, w, G_mu, G_Sigma, eta):
prob = gv.sigmoid(w + gv.logit(Z))
llh = np.sum(supp * X * clog(prob)) + np.sum(supp * (1 - X) * clog(1 - prob))
prior = logpdf(G, G_mu, G_Sigma)
#print llh, prior
return llh + prior
def fun(w0):
return gv.sigmoid(w0 +
gv.logit(Zs_ok[..., f])).mean() - Xbar[l0,
l1, f]
def fun(w0):
return gv.sigmoid(w0 + gv.logit(Zs_ok[...,f])).mean() - Xbar[l0,l1,f]
