Central subjects in probability theory include discrete and continuous random variables, probability distributions, and stochastic processes, which provide mathematical abstractions of non-deterministic or uncertain processes or measured quantities that may either be single occurrences or evolve over time in a random fashion.

Although it is not possible to perfectly predict random events, much can be said about their behavior. Two major results in probability theory describing such behaviour are the law of large numbers and the central limit theorem.

As a mathematical foundation for statistics, probability theory is essential to many human activities that involve quantitative analysis of data.^{[1]} Methods of probability theory also apply to descriptions of complex systems given only partial knowledge of their state, as in statistical mechanics. A great discovery of twentieth-century physics was the probabilistic nature of physical phenomena at atomic scales, described in quantum mechanics.^{[2]}

The mathematical theory of probability has its roots in attempts to analyze games of chance by Gerolamo Cardano in the sixteenth century, and by Pierre de Fermat and Blaise Pascal in the seventeenth century (for example the "problem of points"). Christiaan Huygens published a book on the subject in 1657^{[3]} and in the 19th century, Pierre Laplace completed what is today considered the classic interpretation.^{[4]}

Initially, probability theory mainly considered **discrete** events, and its methods were mainly combinatorial. Eventually, analytical considerations compelled the incorporation of **continuous** variables into the theory.

This culminated in modern probability theory, on foundations laid by Andrey Nikolaevich Kolmogorov. Kolmogorov combined the notion of sample space, introduced by Richard von Mises, and **measure theory** and presented his axiom system for probability theory in 1933. This became the mostly undisputed axiomatic basis for modern probability theory; but, alternatives exist, such as the adoption of finite rather than countable additivity by Bruno de Finetti.^{[5]}

Most introductions to probability theory treat discrete probability distributions and continuous probability distributions separately. The more mathematically advanced measure theory-based treatment of probability covers the discrete, continuous, a mix of the two, and more.

This page was last edited on 12 June 2018, at 09:43 (UTC).

Reference: https://en.wikipedia.org/wiki/probability_theory under CC BY-SA license.

Reference: https://en.wikipedia.org/wiki/probability_theory under CC BY-SA license.

- Mathematics
- Probability
- Probability Interpretations
- Axioms
- Probability Space
- Measure
- Probability Measure
- Sample Space
- Event
- Random Variables
- Probability Distributions
- Stochastic Processes
- Non-deterministic
- Quantities
- Law Of Large Numbers
- Central Limit Theorem
- Statistics
- [1]
- Statistical Mechanics
- Physics
- Quantum Mechanics
- [2]
- Probability
- Games Of Chance
- Gerolamo Cardano
- Pierre De Fermat
- Blaise Pascal
- Problem Of Points
- Christiaan Huygens
- [3]
- Pierre Laplace
- [4]
- Combinatorial
- Analytical
- Andrey Nikolaevich Kolmogorov
- Sample Space
- Richard Von Mises
- Measure Theory
- Axiom System
- Axiomatic Basis
- Bruno De Finetti
- [5]

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