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  1. onlinelibrary.wiley.com

    Therefore, we introduce Hamiltonian Monte Carlo (HMC), an efficient algorithm that outperforms random-walk methods in exploring complex parameter spaces. We apply HMC to calibrate an SEIR model and frame the process within a practical workflow.
    Author:Jair Andrade, Jim DugganPublished:2021
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  3. researchgate.net

    In their recent work, Andrade et al. Andrade and Duggan (2021) suggest Hamiltonian Monte Carlo as a more efficient algorithm for Bayesian parameter inference in system dynamics, and provide a ...
  4. cordis.europa.eu

    May 2, 2024A Bayesian approach to calibrate system dynamics models using Hamiltonian Monte Carlo System Dynamics Review, Issue Volume37, Issue 4, 2021, Page (s) 283-309, ISSN 0883-7066 Exploring the Effect of Misinformation on Infectious Disease Transmission Systems, Issue 10, 2022, Page (s) 50, ISSN 2079-8954
  5. asmedigitalcollection.asme.org

    Apr 8, 2023A high fidelity simulator is used to emulate the "experiments" and generate the data for the calibration. The merit of this work is not tied to a new Bayesian methodology for calibration, but to the demonstration of how the Bayesian machinery can establish connections among models in computational dynamics, even when the data in use is noisy.
  6. sciencedirect.com

    Oct 15, 2024We fit this model to daily confirmed COVID-19 cases in Tennessee, United States of America (USA), from June 4 to November 26, 2021, in a Bayesian inference approach using the Hamiltonian Monte Carlo (HMC) algorithm.
  7. This paper considers Bayesian parameter estimation of dynamic systems using a Markov Chain Monte Carlo (MCMC) approach. The Metroplis-Hastings (MH) algorithm is employed, and the main contribution of the paper is to examine and illustrate the efficacy of a particular proposal density based on energy preserving Hamiltonian dynamics, which results in what is known in the statistics literature as ...
  8. Therefore, we introduce Hamiltonian Monte Carlo (HMC), an efficient algorithm that outperforms random-walk methods in exploring complex parameter spaces. We apply HMC to calibrate an SEIR model and frame the process within a practical workflow.

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