= Likelihood weighting
<Likelihood weighting> uses observation <likelihoods> as <importance weights>. If parameters are drawn independently from their <prior distribution> $\rho$, weight a draw by $L(\theta)$ and estimate a posterior <expectation> of $h$ by $\sum_jL(\theta_j)h(\theta_j)/\sum_jL(\theta_j)$. With positive finite evidence and integrable weighted quantities, the <strong law of large numbers> proves consistency of this <self-normalized importance sampling> ratio. Finite-sample <bias> and concentration of the weights remain possible. A different proposal requires the additional <prior density>-to-proposal density ratio.
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