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1 results

  • 2 - Dynamical Systems and Markov Processes

  • from Part I - Concepts from Modeling, Inference, and Computing
  • Steve Pressé, Arizona State University, Ioannis Sgouralis, University of Tennessee, Knoxville
  • Book:
    Data Modeling for the Sciences
    Published online:
    17 August 2023
    Print publication:
    31 August 2023, pp 40-107

  • Summary

    In this chapter we present dynamical systems and their probabilistic description. We distinguish between system descriptions with discrete and continuous state-spaces as well as discrete and continuous time. We formulate examples of statistical models including Markov models, Markov jump processes, and stochastic differential equations. In doing so, we describe fundamental equations governing the evolution of the probability of dynamical systems. These equations include the master equation, Langevin equation, and Fokker–Plank equation. We also present sampling methods to simulate realizations of a stochastic dynamical process such as the Gillespie algorithm. We end with case studies relevant to chemistry and physics.