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20232026
most citedPartition function approach to non-Gaussian likelihoods: information theory and state variables for Bayesian inference

1 citations · 1 across the 6 of their papers we have counts for

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6 papers

hep-ph2026

The Latent Information Geometry of Jet Classification

Rebecca Maria Kuntz, Tilman Plehn, Björn Malte Schäfer +2

Latent representations are an important theme in modern machine learning. Any network training with the notion of locality introduces a latent geometry which we can analyze with th…

math.ST2025

Rényi-Induced Information Geometry and Hartigan's Prior Family

Rebecca Maria Kuntz, Heinrich von Campe, Björn Malte Schäfer

We derive the information geometry induced by the statistical Rényi divergence, namely its metric tensor, its dual parametrized connections, as well as its dual Laplacians. Based o…

cond-mat.stat-mech2024★ 1 cited

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…

astro-ph.CO2023

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…

astro-ph.CO2023

Partition function approach to non-Gaussian likelihoods: partitions for the inference of functions and the Fisher-functional

Rebecca Maria Kuntz, Maximilian Philipp Herzog, Heinrich von Campe +2

Motivated by constraints on the dark energy equation of state from supernova-data, we propose a formalism for the Bayesian inference of functions: Starting at a functional variant…

astro-ph.CO2023

Partition function approach to non-Gaussian likelihoods: physically motivated convergence criteria for Markov-chains

Lennart Röver, Heinrich von Campe, Maximilian Philipp Herzog +2

Non-Gaussian distributions in cosmology are commonly evaluated with Monte Carlo Markov-chain methods, as the Fisher-matrix formalism is restricted to the Gaussian case. The Metropo…