Potential interesting ESS improvement based on mixture of Gaussians

[yebai]

The paper below proposes an interesting extension of ESS that handles models with non-Gaussian prior, and models with Gaussian priors but with informative likelihoods. The authors also motivate their algorithm for parallelisable implementation. If this work well in practice, it could be an interesting gradient-free alternative to the HMC/NUTS sampler for low to mid dimensional problems.

Nishihara, R., Murray, I., & Adams, R. P. (2014). Parallel MCMC with Generalized Elliptical Slice Sampling. Journal of Machine Learning Research: JMLR, 15(61), 2087–2112.

@imurray @robertnishihara

[devmotion]

I read the paper quite some time ago but never tried it.