Misc,
Bayesian Computation in Astronomy: Novel methods for parallel and gradient-free inference
(2023)cite arxiv:2303.16134Comment: PhD Thesis, 280 pages.
Abstract
The goal of this thesis is twofold; introduce the fundamentals of Bayesian
inference and computation focusing on astronomical and cosmological
applications, and present recent advances in probabilistic computational
methods developed by the author that aim to facilitate Bayesian data analysis
for the next generation of astronomical observations and theoretical models.
The first part of this thesis familiarises the reader with the notion of
probability and its relevance for science through the prism of Bayesian
reasoning, by introducing the key constituents of the theory and discussing its
best practices. The second part includes a pedagogical introduction to the
principles of Bayesian computation motivated by the geometric characteristics
of probability distributions and followed by a detailed exposition of various
methods including Markov chain Monte Carlo (MCMC), Sequential Monte Carlo
(SMC), and Nested Sampling (NS). Finally, the third part presents two novel
computational methods (Ensemble Slice Sampling and Preconditioned Monte Carlo)
and their respective software implementations (zeus and pocoMC). abridged
