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Entropy
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Monte Carlo Simulation in Statistical Physics
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Monte Carlo Simulation in Statistical Physics

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Statistical Physics".

Deadline for manuscript submissions: 1 July 2024 | Viewed by 3398

Special Issue Editors

Dr. Sergio Curilef

E-Mail Website
Guest Editor
Departamento de Física, Universidad Católica del Norte, Av. Angamos 0610, Antofagasta 3580000, Chile
Interests: systems out of equilibrium; nonlinear phenomena; complex systems; Monte Carlo simulation
Dr. Francisco Calderón

E-Mail Website
Guest Editor
Departamento de Física, Universidad Católica del Norte, Av. Angamos 0610, Antofagasta 3580000, Chile
Interests: statistical physics; plasma physics; complex systems

Special Issue Information

Dear Colleagues,

Monte Carlo simulations are broad computational tools and techniques based on repeated random sampling to obtain numerical results related to problems such as numerical integration, optimization, and generating draws from a probability distribution. They are frequently employed in mathematical and physical systems in cases where the use of other approaches is impossible. Different strategies include modeling phenomena with significant input uncertainty, such as calculating risk in business and mathematics and evaluating multidimensional definite integrals with complicated boundary conditions. This Special Issue aims to showcase simulation of phenomena with significant uncertainty in inputs and systems with many coupled degrees of freedom that have applications in engineering, climate change, computational biology, artificial intelligence for games, applied statistics, and stochastic optimization, among other related topics.

Dr. Sergio Curilef
Dr. Francisco Calderón
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • statistical physics
  • complex systems
  • risk
  • economic modeling
  • biology models
  • climate change
  • stochastic optimization

Published Papers (2 papers)

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Research

20 pages, 518 KiB  
Article
Asymptotic Expansion and Weak Approximation for a Stochastic Control Problem on Path Space
by Masaya Kannari, Riu Naito and Toshihiro Yamada
Entropy 2024, 26(2), 119; https://doi.org/10.3390/e26020119 - 29 Jan 2024
Viewed by 758
Abstract
The paper provides a precise error estimate for an asymptotic expansion of a certain stochastic control problem related to relative entropy minimization. In particular, it is shown that the expansion error depends on the regularity of functionals on path space. An efficient numerical [...] Read more.
The paper provides a precise error estimate for an asymptotic expansion of a certain stochastic control problem related to relative entropy minimization. In particular, it is shown that the expansion error depends on the regularity of functionals on path space. An efficient numerical scheme based on a weak approximation with Monte Carlo simulation is employed to implement the asymptotic expansion in multidimensional settings. Throughout numerical experiments, it is confirmed that the approximation error of the proposed scheme is consistent with the theoretical rate of convergence. Full article
(This article belongs to the Special Issue Monte Carlo Simulation in Statistical Physics)
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27 pages, 462 KiB  
Article
Stochastic Expectation Maximization Algorithm for Linear Mixed-Effects Model with Interactions in the Presence of Incomplete Data
by Alandra Zakkour, Cyril Perret and Yousri Slaoui
Entropy 2023, 25(3), 473; https://doi.org/10.3390/e25030473 - 08 Mar 2023
Cited by 1 | Viewed by 1355
Abstract
The purpose of this paper is to propose a new algorithm based on stochastic expectation maximization (SEM) to deal with the problem of unobserved values when multiple interactions in a linear mixed-effects model (LMEM) are present. We test the effectiveness of the proposed [...] Read more.
The purpose of this paper is to propose a new algorithm based on stochastic expectation maximization (SEM) to deal with the problem of unobserved values when multiple interactions in a linear mixed-effects model (LMEM) are present. We test the effectiveness of the proposed algorithm with the stochastic approximation expectation maximization (SAEM) and Monte Carlo Markov chain (MCMC) algorithms. This comparison is implemented to highlight the importance of including the maximum effects that can affect the model. The applications are made on both simulated psychological and real data. The findings demonstrate that our proposed SEM algorithm is highly preferable to the other competitor algorithms. Full article
(This article belongs to the Special Issue Monte Carlo Simulation in Statistical Physics)
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