def mh_wp_func(pkd_x): (x, meta, beliefs, state, params) = pkd_x if params['prior_wp_sm'] == "only_bij" and not fwd.bijection(state['sm'], x)[0]: return float(-np.inf) mll = meta_loglike_marg(meta, state['world'], state['sm'], x, state['meta_noise'], params) lpw = log_prob_world(state['world'], x) if params['binary_beliefs']: bll = beliefs_loglike_binary(beliefs, state['signals'], state['sm'], x, params['own_noise_type'], state['own_noise']) return mll + lpw + bll else: bll = beliefs_loglike_marg(beliefs, state['world'], state['sm'], x, params['own_noise_type'], state['own_noise']) return mll + lpw + bll
def mh_sm_func(pkd_x): (x, meta, beliefs, state, params) = pkd_x if (params['prior_wp_sm'] == "only_bij" and not fwd.bijection(x, state['wp'])[0]): return float(-np.inf) #if experts: # lps = log_prob_signals_expertise(state['signals'], state['world'], # x, state['expertise']) #else: # lps = log_prob_signals(state['signals'], state['world'], x) mll = meta_loglike_marg(meta, state['world'], x, state['wp'], state['meta_noise'], params) if params['binary_beliefs']: bll = beliefs_loglike_binary(beliefs, state['signals'], x, state['wp'], params['own_noise_type'], state['own_noise']) else: bll = beliefs_loglike_marg(beliefs, state['world'], x, state['wp'], params['own_noise_type'], state['own_noise']) return mll + bll