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]