## Harmonic means, again

Over the non-vacation and the vacation breaks of the past weeks, I skipped a lot of arXiv postings. This morning, I took a look at “Probabilities of exoplanet signals from posterior samplings” by Mikko Tuomi and Hugh R. A. Jones. This is a paper to appear in Astronomy and Astrophysics, but the main point [to me] is to study a novel approximation to marginal likelihood. The authors propose what looks at first as defensive sampling: given a likelihood f(x|θ) and a corresponding Markov chaini), the approximation is based on the following importance sampling representation

$\hat m(x) = \sum_{i=h+1}^N \dfrac{f(x|\theta_i)}{(1-\lambda) f(x|\theta_i) + \lambda f(x|\theta_{i-h})}\Big/$

$\sum_{i=h+1}^N \dfrac{1}{(1-\lambda) f(x|\theta_i) + \lambda f(x|\theta_{i-h})}$

This is called a truncated posterior mixture approximation and, under closer scrutiny, it is not defensive sampling. Indeed the second part in the denominators does not depend on the parameter θi, therefore, as far as importance sampling is concerned, this is a constant (if random) term! The authors impose a bounded parameter space for this reason, however I do not see why such an approximation is converging. Except when λ=0, of course, which brings us back to the original harmonic mean estimator. (Properly rejected by the authors for having a very slow convergence. Or, more accurately, generally no stronger convergence than the law of large numbers.)  Furthermore, the generic importance sampling argument does not work here since, if

$g(\theta) \propto (1-\lambda) \pi(\theta|x) + \lambda \pi(\theta_{i-h}|x)$

is the importance function, the ratio

$\dfrac{\pi(\theta_i)f(x|\theta_i)}{(1-\lambda) \pi(\theta|x) + \lambda \pi(\theta_{i-h}|x)}$

does not simplify… I do not understand either why the authors compare Bayes factors approximations based on this technique, on the harmonic mean version or on Chib and Jeliazkov’s (2001) solution with both DIC and AIC, since the later are not approximations to the Bayes factors. I am therefore quite surprised at the paper being accepted for publication, given that the numerical evaluation shows the impact of the coefficient λ does not vanish with the number of simulations. (Which is logical given the bias induced by the additional term.)

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