[more than] everything you always wanted to know about marginal likelihood

Earlier this year, F. Llorente, L. Martino, D. Delgado, and J. Lopez-Santiago have arXived an updated version of their massive survey on marginal likelihood computation. Which I can only warmly recommend to anyone interested in the matter! Or looking for a base camp to initiate a graduate project. They break the methods into four families

  1. Deterministic approximations (e.g., Laplace approximations)
  2. Methods based on density estimation (e.g., Chib’s method, aka the candidate’s formula)
  3. Importance sampling, including sequential Monte Carlo, with a subsection connecting with MCMC
  4. Vertical representations (mostly, nested sampling)

Besides sheer computation, the survey also broaches upon issues like improper priors and alternatives to Bayes factors. The parts I would have done in more details are reversible jump MCMC and the long-lasting impact of Geyer’s reverse logistic regression (with the noise contrasting extension), even though the link with bridge sampling is briefly mentioned there. There is even a table reporting on the coverage of earlier surveys. Of course, the following postnote of the manuscript

The Christian Robert’s blog deserves a special mention , since Professor C. Robert has devoted several entries of his blog with very interesting comments regarding the marginal likelihood estimation and related topics.

does not in the least make me less objective! Some of the final recommendations

  • use of Naive Monte Carlo [simulate from the prior] should be always considered [assuming a proper prior!]
  • a multiple-try method is a good choice within the MCMC schemes
  • optimal umbrella sampling estimator is difficult and costly to implement , so its best performance may not be achieved in practice
  • adaptive importance sampling uses the posterior samples to build a suitable normalized proposal, so it benefits from localizing samples in regions of high posterior probability while preserving the properties of standard importance sampling
  • Chib’s method is a good alternative, that provide very good performances [but is not always available]
  • the success [of nested sampling] in the literature is surprising.

One Response to “[more than] everything you always wanted to know about marginal likelihood”

  1. Fernando Llorente Says:

    Dear Prof. Robert, thanks for the very kind words, it was an honor for all of us! Your blog was very useful support during the preparation of the manuscript.

    I’m looking forward to another edition of the “estimating constants” workshop! I hope to get to meet you eventually at some conference.


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