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New method uses virtual particles for recursive maximum likelihood estimation

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed a new method for recursive maximum likelihood estimation in stochastic interacting particle systems. This technique focuses on optimizing the stationary log-likelihood of the limiting mean-field system when direct consistent estimation is not feasible. The approach utilizes stochastic gradient estimates derived from a single observed particle's trajectory, along with virtual particle systems, to converge towards stationary points of the mean-field system. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Introduces a novel statistical estimation technique applicable to complex systems, potentially impacting AI model training and analysis.

RANK_REASON This is a research paper detailing a new statistical estimation method for interacting particle systems.

Read on arXiv stat.ML →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 · Louis Sharrock, Nikolas Kantas, Grigorios A. Pavliotis ·

    Recursive Maximum Likelihood Estimation for Interacting Particle Systems using Virtual Particles

    arXiv:2605.00786v1 Announce Type: cross Abstract: We study recursive maximum likelihood estimation for stochastic interacting particle systems based on continuous observation of a single particle. In this regime, consistent estimation of the finite-particle log-likelihood is not …

  2. arXiv stat.ML TIER_1 · Grigorios A. Pavliotis ·

    Recursive Maximum Likelihood Estimation for Interacting Particle Systems using Virtual Particles

    We study recursive maximum likelihood estimation for stochastic interacting particle systems based on continuous observation of a single particle. In this regime, consistent estimation of the finite-particle log-likelihood is not possible, even in the limit as the number of parti…

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