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arXiv · 2311.16218

Partition function approach to non-Gaussian likelihoods: macrocanonical partitions and replicating Markov-chains

Maximilian Philipp Herzog·Heinrich von Campe·Rebecca Maria Kuntz·Lennart Röver·Björn Malte Schäfer
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Abstract

Monte-Carlo techniques are standard numerical tools for exploring non-Gaussian and multivariate likelihoods. Many variants of the original Metropolis-Hastings algorithm have been proposed to increase the sampling efficiency. Motivated by Ensemble Monte Carlo we allow the number of Markov chains to vary by exchanging particles with a reservoir, controlled by a parameter analogous to a chemical potential $μ$, which effectively establishes a random process that samples microstates from a macrocanonical instead of a canonical ensemble. In this paper, we develop the theory of macrocanonical sampling for statistical inference on the basis of Bayesian macrocanonical partition functions, thereby bringing to light the relations between information-theoretical quantities and thermodynamic properties. Furthermore, we propose an algorithm for macrocanonical sampling, $\texttt{Avalanche Sampling}$, and apply it to various toy problems as well as the likelihood on the cosmological parameters $Ω_m$ and $w$ on the basis of data from the supernova distance redshift relation.

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Maximilian Philipp Herzog, Heinrich von Campe, Rebecca Maria Kuntz, Lennart Röver, Björn Malte Schäfer. 2024-10-23. Partition function approach to non-Gaussian likelihoods: macrocanonical partitions and replicating Markov-chains. https://doi.org/10.33232/001c.125132

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