Probabilistic Models for Dynamical Systems

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

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