69 citations · 128 across the 5 of their papers we have counts for
5 papers
Flow Annealed Kalman Inversion for Gradient-Free Inference in Bayesian Inverse Problems
Richard D. P. Grumitt, Minas Karamanis, Uroš Seljak
For many scientific inverse problems we are required to evaluate an expensive forward model. Moreover, the model is often given in such a form that it is unrealistic to access its…
Bayesian Computation in Astronomy: Novel methods for parallel and gradient-free inference
Minas Karamanis
The goal of this thesis is twofold; introduce the fundamentals of Bayesian inference and computation focusing on astronomical and cosmological applications, and present recent adva…
JAX-COSMO: An End-to-End Differentiable and GPU Accelerated Cosmology Library
Jean-Eric Campagne, François Lanusse, Joe Zuntz +7
We present jax-cosmo, a library for automatically differentiable cosmological theory calculations. It uses the JAX library, which has created a new coding ecosystem, especially in…
pocoMC: A Python package for accelerated Bayesian inference in astronomy and cosmology
Minas Karamanis, David Nabergoj, Florian Beutler +2
pocoMC is a Python package for accelerated Bayesian inference in astronomy and cosmology. The code is designed to sample efficiently from posterior distributions with non-trivial g…
Accelerating astronomical and cosmological inference with Preconditioned Monte Carlo
Minas Karamanis, Florian Beutler, John A. Peacock +2
We introduce Preconditioned Monte Carlo (PMC), a novel Monte Carlo method for Bayesian inference that facilitates efficient sampling of probability distributions with non-trivial g…