Hi,
This package looks great. I wonder if you have considered using the adaptation schemes from Syed 2019 and Syed 2021.
I have implemented the first of those parallel tempering schemes in C++ and interfaced it with Stan to do some high-dimensional sampling for the EHT and found orders of magnitude improvements over other adaptation schemes. There are also many improvements in the second reference that should drastically increase the round-trip rate of the sampler.
@s-syed and I were actually planning on implementing a generic parallel tempering algorithm for Julia, but it looks like you have a great package here. Did you want to join forces? Having a state-of-the-art PT package in Julia that works with most of AbstractMCMC.jl would be pretty cool.
Hi,
This package looks great. I wonder if you have considered using the adaptation schemes from Syed 2019 and Syed 2021.
I have implemented the first of those parallel tempering schemes in C++ and interfaced it with Stan to do some high-dimensional sampling for the EHT and found orders of magnitude improvements over other adaptation schemes. There are also many improvements in the second reference that should drastically increase the round-trip rate of the sampler.
@s-syed and I were actually planning on implementing a generic parallel tempering algorithm for Julia, but it looks like you have a great package here. Did you want to join forces? Having a state-of-the-art PT package in Julia that works with most of AbstractMCMC.jl would be pretty cool.