465 citations · 776 across the 17 of their papers we have counts for
18 papers
Learned proposals in trans-dimensional inference are optimal at equilibrium, not during assembly
Argyro Sasli, Nikolaos Karnesis, Minas Karamanis +5
Inferring the dimension of a model - the number of components needed to explain data - jointly with the parameters is a pervasive problem, from counting sources in an image to mixt…
Alleviating prior dependencies for DESI DR1 clustering fits through reparameterization
Marco Bonici, Simone Paradiso, Glenn McGee +48
Bayesian analyses of the full-shape clustering of Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) exhibit prior-volume projection effects, whereby weakly constrain…
Beyond Gaussian Assumptions: A new robust statistical framework for gravitational-wave data analysis
Argyro Sasli, Minas Karamanis, Nikolaos Karnesis +4
Many traditional algorithms applied in gravitational-wave astronomy rely on the assumption of Gaussian noise, a condition not always met. To meet this need, this study extends a ro…
Validating Sequential Monte Carlo for Gravitational-Wave Inference
Michael J. Williams, Minas Karamanis, Yilin Luo +1
Nested sampling (NS) is the preferred stochastic sampling algorithm for gravitational-wave inference for compact binary coalenscences (CBCs). It can handle the complex nature of th…
Robust and fast parameter estimation for gravitational waves from binary neutron star merger remnants
Stamatis Vretinaris, Georgios Vretinaris, Christos Mermigkas +2
We present a robust and efficient methodology for parameter estimation of gravitational waves generated during the post-merger phase of binary neutron star mergers. Our approach le…
Selecting samples of galaxies with fewer Fingers-of-God
Antón Baleato Lizancos, Uroš Seljak, Minas Karamanis +2
The radial positions of galaxies inferred from their measured redshift appear distorted due to their peculiar velocities. We argue that the contribution from stochastic velocities…
Persistent Sampling: Enhancing the Efficiency of Sequential Monte Carlo
Minas Karamanis, Uroš Seljak
Sequential Monte Carlo (SMC) samplers are powerful tools for Bayesian inference but suffer from high computational costs due to their reliance on large particle ensembles for accur…
Sequential Kalman Tuning of the -preconditioned Crank-Nicolson algorithm: efficient, adaptive and gradient-free inference for Bayesian inverse problems
Richard D. P. Grumitt, Minas Karamanis, Uroš Seljak
Ensemble Kalman Inversion (EKI) has been proposed as an efficient method for the approximate solution of Bayesian inverse problems with expensive forward models. However, when appl…
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…
Overview of the Instrumentation for the Dark Energy Spectroscopic Instrument
B. Abareshi, J. Aguilar, S. Ahlen +264
The Dark Energy Spectroscopic Instrument (DESI) has embarked on an ambitious five-year survey to explore the nature of dark energy with spectroscopy of 40 million galaxies and quas…
: Halo Model Emulator for the Galaxy Power Spectrum
Jamie Donald-McCann, Florian Beutler, Kazuya Koyama +1
We present , a suite of neural network based emulators and accompanying Python package that have been developed with the goal of producing fast and accurate pr…
hankl: A lightweight Python implementation of the FFTLog algorithm for Cosmology
Minas Karamanis, Florian Beutler
We introduce hankl, a lightweight Python implementation of the FFTLog algorithm for Cosmology. The FFTLog algorithm is an extension of the Fast Fourier Transform (FFT) for logarith…
zeus: A Python implementation of Ensemble Slice Sampling for efficient Bayesian parameter inference
Minas Karamanis, Florian Beutler, John A. Peacock
We introduce zeus, a well-tested Python implementation of the Ensemble Slice Sampling (ESS) method for Bayesian parameter inference. ESS is a novel Markov chain Monte Carlo (MCMC)…
Ensemble Slice Sampling: Parallel, black-box and gradient-free inference for correlated & multimodal distributions
Minas Karamanis, Florian Beutler
Slice Sampling has emerged as a powerful Markov Chain Monte Carlo algorithm that adapts to the characteristics of the target distribution with minimal hand-tuning. However, Slice S…