Probability Theory

Sigma-algebras, random variables, expectation, and limit theorems.


foundation tier

Probability Theory. Sigma-algebras, random variables, expectation, and limit theorems. This page collects canonical references that organise the subject and provide entry points to its main techniques.

Foundations and canonical references

The standard treatments of probability theory approach the subject from complementary angles. Durrett, Probability: Theory and Examples (2019) is the anchor reference for the subject and lays out the core definitions, theorems, and worked examples that practitioners return to. Fristedt, A Modern Approach to Probability Theory (1997) gives a parallel, more proof-oriented exposition of the same material and is widely used as a graduate text.

Open methodological questions for probability theory include sharpening the bridges between foundational theory and computational practice, extending classical results to broader or more structured settings, and integrating the techniques surveyed above with adjacent mathematical disciplines. The references listed in this page are the entry points that current work builds on.

Prerequisites

Sources

  • textbook · primary · 2019
    Probability: Theory and Examples
    durrett-2019
  • textbook · primary · 1997
    A Modern Approach to Probability Theory
    fristedt-1997, gray-1997

In context

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Explore

  1. 01

    Measure-Theoretic Probability

    Construction of probability measures, Kolmogorov extension, and conditional expectation.

  2. 02

    Limit Theorems

    Laws of large numbers, central limit theorems, and large deviations.

  3. 03

    Concentration Inequalities

    Chernoff, Hoeffding, Bernstein, McDiarmid, and Talagrand bounds.

  4. 04

    Martingale Theory

    Doob decomposition, optional stopping, and convergence theorems.

  5. 05

    Large Deviation Theory

    Cramér, Sanov, Varadhan's lemma, and rate functions.

  6. 06

    Coupling Methods

    Coupling for mixing, Stein's method, and exchangeable pairs.

  7. 07

    Malliavin Calculus

    Stochastic calculus of variations and applications to finance and SPDE.


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