Feature Request: Concomitant Variable Model for priors

[travis-leith]

In the R package flexmix, I can specify a model for the priors, rather than just a vector a numbers. The model can depend on some arbitrary set of associated variables, called concomitant variables.

https://cran.r-project.org/web/packages/flexmix/vignettes/mixture-regressions.pdf

The idea is that there are some explanatory variables which have some information guiding the prior distribution.

 A general model class of finite mixtures of regression models is considered in the following. The
 mixture is assumed to consist of K components where each component follows a parametric
 distribution. Each component has a weight assigned which indicates the a-priori probability
 for an observation to come from this component and the mixture distribution is given by the
 weighted sum over the K components. If the weights depend on further variables, these are
 referred to as concomitant variables.

flexmix is designed to be extensible, but requires some level of expertise in order to achieve such extension. Out of the box, it has only a single concomitant model form. Would be good to have a similar capability for python.

[AlexanderFabisch]

Hi @travis-leith,

sounds interesting, but I currently don't have time to implement this. If you are interested, you could make a pull request. Otherwise I will leave the issue open for anyone to take the task.