Andrew Valentine
apvalentine.bsky.social
Andrew Valentine
@apvalentine.bsky.social
Assoc. Prof., Durham University
Inverse theory, ML, seismology, geophysics & computation.
https://valentineap.github.io/
In particular, if we have already run McMC for each state individually, Carlin & Chib's approach lets us build a trans-conceptual ensemble by resampling the resulting ensembles.

Go read doi.org/10.1029/2024... to find out more and see some examples of this idea applied to real problems!

(4/4)
August 27, 2025 at 12:59 PM
An alternative comes from the work of Carlin & Chib (1999). Instead of working across multiple different parameter spaces, combine them all into a single 'product space'. Of course, this increases the dimension of the search space (= bad), but in practice some simplification may be possible.

(3/4)
August 27, 2025 at 12:59 PM
In some cases this problem can be tackled using reversible-jump McMC (Green, 1995) to switch between the different physical theories (or 'conceptual states'). See, e.g., 'trans-dimensional' sampling. However, this requires us to know the Jacobian of the transformation between states...

(2/4)
August 27, 2025 at 12:59 PM
👋 Can I be added to the science feed, please? This is me: scholar.google.com/citations?us...
Andrew Valentine
‪Department of Earth Sciences, Durham University‬ - ‪‪Cited by 894‬‬ - ‪Inverse theory‬ - ‪Machine learning‬ - ‪Geophysics‬ - ‪Seismology‬ - ‪Computational science‬
scholar.google.com
October 10, 2024 at 4:15 PM