'Compute the log-likelihood under the distribution for a given time t,
not the whole sequence.
Args:
z_t_bxu: sample to compute likelihood for at time t.
z_tm1_bxu (optional): sample condition probability of z_t upon.
Returns:
The likelihood of p_t under the model at time t. i.e.
p(z_t|z_tm1) = N(z_tm1 * phis, eps^2)'

def logp_t(self, z_t_bxu, z_tm1_bxu=None):

    if (z_tm1_bxu is None):
        return diag_gaussian_log_likelihood(z_t_bxu, self.pmeans_bxu, self.logpvars_bxu)
    else:
        means_t_bxu = (self.pmeans_bxu + (self.phis_bxu * z_tm1_bxu))
        logp_tgtm1_bxu = diag_gaussian_log_likelihood(z_t_bxu, means_t_bxu, self.logevars_bxu)
        return logp_tgtm1_bxu
