Asymptotic bias of inexact Markov Chain Monte Carlo methods in high dimension
Inexact Markov chain Monte Carlo methods rely on Markov chains that do not exactly
preserve the target distribution. Examples include the unadjusted Langevin algorithm (ULA) …
preserve the target distribution. Examples include the unadjusted Langevin algorithm (ULA) …
[HTML][HTML] Differentiable and accelerated spherical harmonic and Wigner transforms
Many areas of science and engineering encounter data defined on spherical manifolds.
Modelling and analysis of spherical data often necessitates spherical harmonic transforms …
Modelling and analysis of spherical data often necessitates spherical harmonic transforms …
Geodesic slice sampling on the sphere
M Habeck, M Hasenpflug, S Kodgirwar… - arXiv preprint arXiv …, 2023 - arxiv.org
Probability measures on the sphere form an important class of statistical models and are
used, for example, in modeling directional data or shapes. Due to their widespread use, but …
used, for example, in modeling directional data or shapes. Due to their widespread use, but …
[PDF][PDF] PxMCMC: A Python package for proximal Markov Chain Monte Carlo
A Marignier - Journal of Open Source Software, 2023 - joss.theoj.org
Summary Markov Chain Monte Carlo (MCMC) methods form the dominant set of algorithms
for Bayesian inference. The appeal of MCMC in the physical sciences is that it produces a …
for Bayesian inference. The appeal of MCMC in the physical sciences is that it produces a …
Sparse Bayesian mass-mapping using trans-dimensional MCMC
Uncertainty quantification is a crucial step of cosmological mass-mapping that is often
ignored. Suggested methods are typically only approximate or make strong assumptions of …
ignored. Suggested methods are typically only approximate or make strong assumptions of …