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Everything about Diffusion process

Solution to a stochastic differential equation

In probability theory and statistics, diffusion processes are a class of continuous-time Markov process with almost surely continuous sample paths. Diffusion processes are stochastic in nature and hence are used to model many real-life stochastic systems. Brownian motion, reflected Brownian motion and Ornstein–Uhlenbeck processes are examples of diffusion processes. It is used heavily in statistical physics, statistical analysis, information theory, data science, neural networks, finance and marketing. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Diffusion processA 5-minute orientation
  2. Skim a recent paper: Rate enhancement of gated drift-diffusion process by optimal resettingInfluential work since 2022
  3. Pick up a classic book: The Tipping PointThe most-reprinted book on the subject
  4. Read the landmark paper: A New Vertical Diffusion Package with an Explicit Treatment of Entrainment ProcessesCited 6,216 times
  5. Go sideways: Feynman–Kac formulaA closely related topic
  6. Go sideways: Stochastic analysis on manifoldsA closely related topic

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