## mixed feelings

Posted in Books, Kids, Statistics with tags , , , , on September 9, 2021 by xi'an

Two recent questions on X validated about mixtures:

1. One on the potential negative explosion of the E function in the EM algorithm for a mixture of components with different supports:  “I was hoping to use the EM algorithm to fit a mixture model in which the mixture components can have differing support. I’ve run into a problem during the M step because the expected log-likelihood can be [minus] infinite” Which mistake is based on a confusion between the current parameter estimate and the free parameter to optimise.
2. Another one on the Gibbs sampler apparently failing for a two-component mixture with only the weights unknown, when the components are close to one another:  “The algorithm works fine if $$σ$$ is far from $$1$$ but it does not work anymore for $$σ$$ close to $$1$$.” Which did not see a wide posterior as a possible posterior when both components are similar and hence delicate to distinguish from one another.

## skipping sampler

Posted in Books, Statistics, University life with tags , , , , on June 13, 2019 by xi'an

“The Skipping Sampler is an adaptation of the MH algorithm designed to sample from targets which have areas of zero density. It ‘skips’ across such areas, much as a flat stone can skip or skim repeatedly across the surface of water.”

An interesting challenge is simulating from a density restricted to a set C when little is known about C, apart from a mean to check whether or not a given value x is in C or not. John Moriarty, Jure Vogrinc (University of Warwick), and Alessandro Zocca make a new proposal to address this problem in a recently arXived paper. Which somewhat reminded me of the delayed rejection methods proposed by Antonietta Mira. And of our pinball sampler.

The paper spends a large amount of space about transferring from the Euclidean representation of the symmetric proposal density q to its polar representation. Which is rather trivial, but brings the questions of the efficient polar proposals  and of selecting the right type of Euclidean distance for the intended target. The method proposed therein is to select a direction first and keep skipping step by step in that direction until the set C is met again (re-entered). Or until a stopping (halting) boundary has been hit. This makes for a more complex proposal than usual but somewhat surprisingly the symmetry in q is sufficient to make the acceptance probability only depend on the target density.

While the convergence is properly established, I wonder at the practicality of the approach when compared with a regular random walk Metropolis algorithm in that both require a scaling to the jump that relates to the support of the target. Neither too small nor too large. If the set C is that unknown that only local (in or out) information is available, scaling of the jumps (and of the stopping rule) may prove problematic. In equivalent ways for both samplers. In a completely blind exploration, sequential (or population) Monte Carlo would seem more appropriate, at least to learn about the scale of jumps and location of the set C. If this set is defined as an intersection of constraints, a tempered (and sequential) solution would be helpful.  When checking the appurtenance to C becomes a computational challenge, more advance schemes have to be constructed, I would think.