We present a case-study demonstrating the usefulness of Bayesian hierarchical mixture modelling for investigating cognitive processes.
We consider instead variational approximations of the Gibbs posterior, which are fast to compute.
We also extend our method to a class of non-linear score functions, essentially leading to a nonparametric procedure, by considering a Gaussian process prior.
Here we show that inferring the parameters of a unnormalised model on a space $\Omega$ can be mapped onto an equivalent problem of estimating the intensity of a Poisson point process on $\Omega$.
While the behaviour of algorithms based on nuclear norm minimization is now well understood, an as yet unexplored avenue of research is the behaviour of Bayesian algorithms in this context.