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New HMC algorithms detailed in arXiv papers

By PulseAugur Editorial · [2 sources] ·

Two new papers explore advanced Hamiltonian Monte Carlo (HMC) algorithms for statistical modeling. The first paper details the Microcanonical Hamiltonian Monte Carlo algorithm, demonstrating its connection to thermodynamic ensembles and its fulfillment of the Helmholtz theorem, while also proposing a new sampling method for lower-dimensional problems. The second paper introduces Smoothed Picard Hamiltonian Monte Carlo, a low-accuracy sampler that combines Gaussian smoothing, Picard iteration, and higher-order discretization, and presents a framework for upgrading its divergence guarantees to high-accuracy sampling. AI

IMPACT These papers advance theoretical understanding and algorithmic efficiency in statistical sampling methods, potentially impacting AI research that relies on complex probabilistic modeling.

RANK_REASON Two academic papers published on arXiv detailing novel algorithms in Hamiltonian Monte Carlo.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New HMC algorithms detailed in arXiv papers

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Heinrich von Campe, Bjoern Malte Schaefer ·

    Microcanonical Hamiltonian Monte Carlo and the Helmholtz Theorem

    arXiv:2609.07620v1 Announce Type: cross Abstract: 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 demonstrate how thermodynamical state variables and …

  2. arXiv cs.LG TIER_1 English(EN) · Fan Chen, Sinho Chewi, Jianfeng Lu, Matthew S Zhang ·

    Smoothed Picard Hamiltonian Monte Carlo

    arXiv:2609.06906v1 Announce Type: cross Abstract: We develop a new low-accuracy sampler, called \emph{smoothed Picard Hamiltonian Monte Carlo}, which combines Gaussian smoothing, Picard iteration, and higher-order discretization. For a log-concave target $\pi \propto \exp(-V)$ in…

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