(6/6) State-of-the art in nested sampling and MCMC: literature review
Channel: JOHANNES BUCHNER
Date: 02/12/2021

Starting at 2:34, recent developments in:

nested sampling:
* likelihood-restricted prior sampling methods
* methods for high-dimensional problems (over 50 parameters)
* Nested sampling diagnostics
* Parallelisation to computing clusters
* Classification of problems: peculiar shapes, multiple modes, high dimensionality, high information gain, phase transitions
* Nested sampling with smooth borders, Hamiltonian extensions

* Learning efficient Importance sampling proposals

MCMC (gradient-free):
* Affine-invariant Ensemble Sampler (Goodman-Weare) and its breakdown in high dimension
* Ensemble slice sampling

* UltraNest

from Block I: Monte Carlo inference methods
of the course "Practical Inference for Researchers in the Physical Sciences"
https://johannesbuchner.github.io/PracticalInferenceForResearchersInThePhysicalSciencesCourse/
aimed at the graduate and post-graduate level (master, PhD, postdoc)
for physicists, cosmologists, astronomers, etc.
at TU Munich, 2021

Lecturer: Johannes Buchner