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Hamiltonian Monte Carlo explained from a probabilistic perspective

This article delves into Hamiltonian Monte Carlo (HMC), a sophisticated algorithm that powers modern Bayesian inference and statistical machine learning frameworks like PyMC. While originating from physics, HMC is a variant of Markov Chain Monte Carlo (MCMC) that has found applications in diverse fields, including epidemiology and wildfire modeling. The content aims to reverse-engineer HMC, explaining its probabilistic underpinnings and how it generates posterior distributions, moving beyond the typical physics-based motivation. AI

IMPACT Provides a deeper understanding of a core algorithm used in modern Bayesian inference and machine learning.

RANK_REASON The cluster discusses a technical paper explaining a specific algorithm (Hamiltonian Monte Carlo) from a probabilistic perspective.

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Hamiltonian Monte Carlo explained from a probabilistic perspective

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The cluster discusses a technical paper explaining a specific algorithm (Hamiltonian Monte Carlo) from a probabilistic perspective.
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2 independent sources
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paper, other
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High
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47 days old
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COVERAGE [2]

  1. Towards AI TIER_1 English(EN) · Ruiz Rivera ·

    Reverse-Engineering Hamiltonian Monte Carlo: The MCMC Engine Behind Modern Bayesian Inference

    <figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*eWAOo7ph7bwzBFZVwTJqdQ.jpeg" /><figcaption>Photo by <a href="https://unsplash.com/@seitamaaphotography?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText">Sandra Seitamaa</a> on <a href="h…

  2. r/MachineLearning TIER_1 English(EN) · /u/aybehrouz ·

    Notes on Hamiltonian Monte Carlo from a purely probabilistic perspective [P]

    <!-- SC_OFF --><div class="md"><p>I’ve been studying Hamiltonian Monte Carlo and wrote a set of notes explaining HMC without relying on the usual physics-based motivation.</p> <p>The notes develop HMC from a probabilistic/MCMC perspective, starting from introducing an auxiliary v…