9 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…
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…
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…
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…