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.
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.
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.