= 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
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