## Markov melding

Posted in Books, Statistics, University life with tags , , , on July 2, 2020 by xi'an

“An alternative approach is to model smaller, simpler aspects of the data, such that designing these submodels is easier, then combine the submodels.”

An interesting paper by Andrew Manderson and Robert Goudie I read on arXiv on merging (or melding) several models together. With different data and different parameters. The assumption is one of a common parameter φ shared by all (sub)models. Since the product of the joint distributions across the m submodels involves m replicates of φ, the melded distribution is the product of the conditional distributions given φ, times a common (or pooled) prior on φ. Which leads to a perfectly well-defined joint distribution provided the support of this pooled prior is compatible with all conditionals.

The MCMC aspects of such a target are interesting in that the submodels can easily be exploited to return proposal distributions on their own parameters (plus φ). Although the notion is fraught with danger when considering a flat prior on φ, since the posterior is not necessarily well-defined. Or at the very least unrelated with the actual marginal posterior. This first stage is used to build a particle approximation to the posterior distribution of φ, exploited in the later simulation of the other subsample parameters and updates of φ. Due to the rare availability of the (submodel) marginal prior on φ, it is replaced in the paper by a kernel density estimate. Not a great idea as (a) it is unstable and (b) the joint density is costly, while existing! Which brings the authors to set a goal of estimating a ratio. Of the same marginal density in two different values of φ. (Not our frequent problem of the ratio of different marginals!) They achieve this by targeting another joint, using a weight function both for the simulation and the kernel density estimation… Requiring the calibration of the weight function and the production of a biased estimate of the ratio.

While the paper concentrates very much on computational improvements, including the possible recourse to unbiased MCMC, I also feel it is missing on the Bayesian aspects, since the construction of the multi-level Bayesian model faces many challenges. In a sense this is an alternative to our better together paper, where cuts are used to avoid the duplication of common parameters.