 Likelihood reconstruction from posterior simulation

For data $x$, a positive prior distribution density $\pi$ and likelihood function $\ell$, Bayes' theorem gives posterior density $h(\theta \mid x)=\pi (\theta )\ell (\theta )/m(x)$. Thus $h/\pi$ reconstructs the likelihood up to a constant. Estimate the posterior density from simulation, divide by the known prior density and maximize over the search region. A posterior mode alone instead estimates a maximum a posteriori estimate; it estimates a likelihood maximizer only when the prior is constant there. Prior support must include the likelihood maximizer of interest.

// Target: biology.bigb