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Stochastic Processes
Stochastic processes examine systems that evolve over time with inherent randomness, making them central to modern probability and statistics. These books cover foundational theory, advanced modeling techniques, and real-world applications, highlighting how random variables change across discrete or continuous stages. This category is essential for those seeking to analyze unpredictable phenomena, from finance and engineering to biology and computer science.
To navigate within stochastic processes, consider focusing on domains such as Markov chains, Poisson processes, or Brownian motion. Each subfield emphasizes distinct mathematical tools and problem types. For a broader look at related concepts and methods, the wider Probability & Statistics category connects stochastic processes to other quantitative approaches and theoretical frameworks.
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Stochastic Pdes and Modelling of Multiscale Complex System
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Probability: Theory, Examples, Problems, Simulations: Examples
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Genealogies of Interacting Particle Systems
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Probability: Theory, Examples, Problems, Simulations: Examples
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A First Look at Stochastic Processes
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A First Look at Stochastic Processes
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Hilbert and Banach Space-Valued Stochastic Processes
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Universal Theory for Strong Limit Theorems of Probability
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An Unbounded Experience in Random Walks with Applications
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An Introduction to Probability: With Mathematica
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An Introduction to Probability: With Mathematica
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Inform Intro Stoch Cal (2nd Ed)
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Inform Intro Stoch Cal (2nd Ed)
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Stationary Stochastic Models: An Introduction
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Special Functions of Fractional Calculus
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A Graduate Course in Probability
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Stochastic Komatu-Loewner Evolutions
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Element of Stocha Model (3rd Ed)
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Element of Stocha Model (3rd Ed)
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A First Course in Probability for Computer and Data Science
- -10%
Distribution Dependent Stochastic Differential Equations
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Stochastic Geometric Analysis with Applications
- -10%
Stochastic Versus Deterministic Systems Iterative Processes
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