5 papers
Starobinsky in Stereo: SKA-CMB Synergy in SBI
Benedikt Schosser, Caroline Heneka, Björn Malte Schäfer
Modern machine learning techniques can unlock the vast cosmological information encoded in forthcoming Square Kilometre Array (SKA) observations. We show that tomographic 21 cm dat…
Complete Classification of Directed Quantum Graphs on M2
Nina Kiefer, Björn Schäfer
In 2022, Gromada and Matsuda classified undirected quantum graphs on the matrix algebra . Later, Wasilweski provided a solid theory of directed quantum graphs which was former…
Approximating non-Gaussian Bayesian partitions with normalising flows: statistics, inference and application to cosmology
Tobias Röspel, Adrian Schlosser, Björn Malte Schäfer
Subject of this paper is the simplification of Markov chain Monte Carlo sampling as used in Bayesian statistical inference by means of normalising flows, a machine learning method…
Partition function approach to non-Gaussian likelihoods: information theory and state variables for Bayesian inference
Rebecca Maria Kuntz, Heinrich von Campe, Tobias Röspel +2
The significance of statistical physics concepts such as entropy extends far beyond classical thermodynamics. We interpret the similarity between partitions in statistical mechanic…
Partition function approach to non-Gaussian likelihoods: macrocanonical partitions and replicating Markov-chains
Maximilian Philipp Herzog, Heinrich von Campe, Rebecca Maria Kuntz +2
Monte-Carlo techniques are standard numerical tools for exploring non-Gaussian and multivariate likelihoods. Many variants of the original Metropolis-Hastings algorithm have been p…