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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

stat.CO2026

Thermodynamic Cyclic Processes with Markov Samplers in Bayesian Inference

Heinrich von Campe, Bjoern Malte Schaefer

The concept of Markov chain Monte Carlo (MCMC) cycles, an analogy to cyclic processes in heat engines, is presented in order to examine Bayesian inference problems. In this effort,…

cond-mat.stat-mech2026

Microcanonical Hamiltonian Monte Carlo and the Helmholtz Theorem

Heinrich von Campe, Bjoern Malte Schaefer

The recently proposed Microcanonical Hamiltonian Monte Carlo algorithm has not yet been studied in detail from a thermodynamic point of view; this work aims to fill that gap. We de…

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…