Just hit the “arXiv streets”:
N. Dalmasso, T. Pospisil, A. B. Lee, R. Izbicki, P. E. Freeman, A. I. Malz, "Conditional Density Estimation Tools in Python and R with applications to photometric redshifts and likelihood-free cosmological inference", arXiv.org > astro-ph > arXiv:1908.11523v1
My interests are different, however, in that I want to borrow their empirical likelihood methods for other applications.
Note parallel multivariate variations on slice sampling are now known, although I’m not aware of work on how well these go.
And, just for information, there’s very recent work on something called generalized elliptical slice sampling with regional pseudo-priors which I have not read.
There is also another 2019 connection to elliptical slice sampling called Bayesian Tensor Filtering which is interesting because:
- David Blei is involved
- It is connected to slice sampling
- It is related to tensor methods in Statistics which I am just studying, after McCullagh and Gross. These have apparently been used in finance under different names for quite a while.