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Probabilistic Models for Dynamical Systems

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 265

Special Issue Editor


E-Mail Website
Guest Editor
Signal Processing Lab, Department of Electrical and Computer Engineering, University of São Paulo, São Carlos 13566-590, Brazil
Interests: dynamic system modeling; probabilistic models; network models; dynamic Bayesian network; complex system models

Special Issue Information

Dear Colleagues,

Probabilistic models employed in the analysis of dynamical systems involve the incorporation of stochastic components inside variables and time-varying systems. These models have the capability to simulate nonlinear and stochastic dynamic models, thus facilitating the examination of their applicability in many areas. Moreover, they are employed in the creation of innovative, dynamic models, which encompass structures that vary over time, as well as in the continuous monitoring of dynamic systems. Probabilistic models are widely utilised in several areas, such as engineering disciplines, robotics, finance, and medical research. Probabilistic models can also be used to estimate the probability of future events or states based on past observations, making them useful in predicting the behaviour of complex systems, such as weather patterns or financial markets. Other methods for probabilistic modelling of dynamical systems include Gaussian processes, Bayesian inference, Monte Carlo simulations, Markov processes using imperfect probability and the incorporation of partially observable variables. The use of probabilistic models can also have ethical implications, particularly in fields such as autonomous vehicles and medical diagnosis. Ongoing research in the field of probabilistic modelling is focused on developing more efficient algorithms and techniques for handling high-dimensional problems, as well as improving the accuracy and interpretability of these models. The practical implementation of probabilistic models for dynamical systems is faced with numerous challenges, including high dimensionality, complex model structures, limited data availability, computationally intensive procedures, model selection, and the existence of uncertainty in data and models.

Dr. Carlos Dias Maciel
Guest Editor

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

  • dynamic system modeling
  • probabilistic models
  • network models
  • dynamic Bayesian network
  • complex system models

Published Papers

This special issue is now open for submission.
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