def logprior(self,prior):
model=2
logprior = 0.
#failure frequencies
logprior += chiamante_statfunc.dbeta(self.eta_arr,prior['arrfail_alpha'],prior['arrfail_beta'],True)
#centroids/sigma
j = self.p[0].argmax()
if j==0: sigma_mu = prior['sigma_mu'][0:2,0:2]
else: sigma_mu = prior['sigma_mu'][4:,4:]
logprior += chiamante_statfunc.diwishart(self.sigma[j],prior['v0'][j],prior['s0'][j],True) + chiamante_statfunc.dmvnorm(self.mu[j],prior['mu0'][j],sigma_mu,True)
return logprior
def chiamante_logprior(prior,parameters,popidx,hwe_prior):
logprior = 0.
model = prior['model']
if hwe_prior:
for idx,ii in enumerate(popidx):
if prior['hwe_var']>0.: logprior += chiamante_statfunc.ddirichlet(parameters['p'][idx],parameters['alpha'][idx],True) + chiamante_statfunc.dbeta(parameters['raf'][idx],prior['raf_alpha'][idx],prior['raf_beta'][idx],True)
else: logprior += chiamante_statfunc.dbeta(parameters['raf'][idx],prior['raf_alpha'][idx],prior['raf_beta'][idx],True)
else: logprior += chiamante_statfunc.ddirichlet(parameters['p'],np.array([1.01,1.01,1.01]),True)
#failure frequencies
logprior += chiamante_statfunc.dbeta(parameters['eta_seq'],prior['seqfail_alpha'],prior['seqfail_beta'],True)
logprior += chiamante_statfunc.dbeta(parameters['eta_array'],prior['arrfail_alpha'],prior['arrfail_beta'],True)
#centroids/sigma
for j in range(3):
if model==1: #sigma_mu = sigma/kappa0
logprior += chiamante_statfunc.diwishart(parameters['sigma'][j],prior['v0'][j],prior['s0'][j],True) + chiamante_statfunc.dmvnorm(parameters['mu'][j],prior['mu0'][j],parameters['sigma'][j]/prior['kappa0'][j],True)
else:
if model==2: #sigma_mu independent of sigma
logprior += chiamante_statfunc.diwishart(parameters['sigma'][j],prior['v0'][j],prior['s0'][j],True) + chiamante_statfunc.dmvnorm(parameters['mu'][j],prior['mu0'][j],prior['sigma_mu'][j],True)
if model==3: # mu prior is 6 dimensional MVN
logprior += chiamante_statfunc.diwishart(parameters['sigma'][j],prior['v0'][j],prior['s0'][j],True)
if model==3: logprior += chiamante_statfunc.dmvnorm(np.hstack(parameters['mu']),np.hstack(prior['mu0']),prior['sigma_mu'],True)
return logprior
