Catalog of articles in probability theory

Probability

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* Catalog of articles
* Probabilists
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This page lists articles related to probability theory. In particular, it lists many articles corresponding to specific probability distributions. Such articles are marked here by a code of the form (X:Y), which refers to number of random variables involved and the type of the distribution. For example (2:DC) indicates a distribution with two random variables, discrete or continuous. Other codes are just abbreviations for topics. The list of codes can be found in the table of contents.

## Contents

* 1 Core probability: selected topics
* 1.1 Basic notions (bsc)
* 1.2 Instructive examples (paradoxes) (iex)
* 1.3 Moments (mnt)
* 1.4 Inequalities (inq)
* 1.5 Markov chains, processes, fields, networks (Mar)
* 1.6 Gaussian random variables, vectors, functions (Gau)
* 1.7 Conditioning (cnd)
* 1.8 Specific distributions (spd)
* 1.9 Empirical measure (emm)
* 1.10 Limit theorems (lmt)
* 1.11 Large deviations (lrd)
* 1.12 Random graphs (rgr)
* 1.13 Random matrices (rmt)
* 1.14 Stochastic calculus (scl)
* 1.15 Malliavin calculus (Mal)
* 1.16 Random dynamical systems (rds)
* 1.17 Analytic aspects (including measure theoretic) (anl)
* 2 Core probability: other articles, by number and type of random variables
* 2.1 A single random variable (1:)
* 2.1.1 Binary (1:B)
* 2.1.2 Discrete (1:D)
* 2.1.3 Continuous (1:C)
* 2.1.4 Real-valued, arbitrary (1:R)
* 2.1.5 Random point of a manifold (1:M)
* 2.1.6 General (random element of an abstract space) (1:G)
* 2.2 Two random variables (2:)
* 2.2.1 Binary (2:B)
* 2.2.2 Discrete (2:D)
* 2.2.3 Continuous (2:C)
* 2.2.4 Real-valued, arbitrary (2:R)
* 2.2.5 General (random element of an abstract space) (2:G)
* 2.3 Three random variables (3:)
* 2.3.1 Binary (3:B)
* 2.3.2 Discrete (3:D)
* 2.3.3 Continuous (3:C)
* 2.4 Finitely many random variables (F:)
* 2.4.1 Binary (F:B)
* 2.4.2 Discrete (F:D)
* 2.4.3 Continuous (F:C)
* 2.4.4 Real-valued, arbitrary (F:R)
* 2.4.5 General (random element of an abstract space) (F:G)
* 2.5 A large number of random variables (finite but tending to infinity) (L:)
* 2.5.1 Binary (L:B)
* 2.5.2 Discrete (L:D)
* 2.5.3 Real-valued, arbitrary (L:R)
* 2.6 An infinite sequence of random variables (S:)
* 2.6.1 Binary (S:B)
* 2.6.2 Discrete (S:D)
* 2.6.3 Continuous (S:C)
* 2.6.4 Real-valued, arbitrary (S:R)
* 2.6.5 General (random element of an abstract space) (S:G)
* 2.7 Uncountably many random variables (continuous-time processes etc) (U:)
* 2.7.1 Discrete (U:D)
* 2.7.2 Continuous (U:C)
* 2.7.3 Real-valued, arbitrary (U:R)
* 2.7.4 General (random element of an abstract space) (U:G)
* 3 Around the core
* 3.1 General aspects (grl)
* 3.2 Foundations of probability theory (fnd)
* 3.3 Gambling (gmb)
* 3.4 Coincidence (cnc)
* 3.5 Algorithmics (alg)
* 3.6 Bayesian approach (Bay)
* 3.7 Financial mathematics (fnc)
* 3.8 Physics (phs)
* 3.9 Genetics (gnt)
* 3.10 Stochastic process (spr)
* 3.11 Geometric probability (geo)
* 3.12 Empirical findings (emp)
* 3.13 Historical (hst)
* 3.14 Miscellany (msc)
* 4 Counters of articles

## Core probability: selected topics

Probability theory

### Basic notions (bsc)

* Random variable
* Continuous probability distribution / (1:C)
* Cumulative distribution function / (1:DCR)
* Discrete probability distribution / (1:D)
* Independent and identically-distributed random variables / (FS:BDCR)
* Joint probability distribution / (F:DC)
* Marginal distribution / (2F:DC)
* Probability density function / (1:C)
* Probability distribution / (1:DCRG)
* Probability distribution function
* Probability mass function / (1:D)
* Sample space

### Instructive examples (paradoxes) (iex)

* Berkson's paradox / (2:B)
* Bertrand's box paradox / (F:B)
* Borel–Kolmogorov paradox / cnd (2:CM)
* Boy or Girl paradox / (2:B)
* Exchange paradox / (2:D)
* Intransitive dice
* Monty Hall problem / (F:B)
* Necktie paradox
* Simpson's paradox
* Sleeping Beauty problem
* St. Petersburg paradox / mnt (1:D)
* Three Prisoners problem
* Two envelopes problem

### Moments (mnt)

* Expected value / (12:DCR)
* Canonical correlation / (F:R)
* Carleman's condition / anl (1:R)
* Central moment / (1:R)
* Coefficient of variation / (1:R)
* Correlation / (2:R)
* Correlation function / (U:R)
* Covariance / (2F:R) (1:G)
* Covariance function / (U:R)
* Covariance matrix / (F:R)
* Cumulant / (12F:DCR)
* Factorial moment / (1:R)
* Factorial moment generating function / anl (1:R)
* Fano factor
* Geometric standard deviation / (1:R)
* Hamburger moment problem / anl (1:R)
* Hausdorff moment problem / anl (1:R)
* Isserlis Gaussian moment theorem / Gau
* Jensen's inequality / (1:DCR)
* Kurtosis / (1:CR)
* Law of the unconscious statistician / (1:DCR)
* Moment / (12FU:CRG)
* Law of total covariance / (F:R)
* Law of total cumulance / (F:R)
* Law of total expectation / (F:DR)
* Law of total variance / (F:R)
* Logmoment generating function
* Marcinkiewicz–Zygmund inequality / inq
* Method of moments / lmt (L:R)
* Moment problem / anl (1:R)
* Moment-generating function / anl (1F:R)
* Second moment method / (1FL:DR)
* Skewness / (1:R)
* St. Petersburg paradox / iex (1:D)
* Standard deviation / (1:DCR)
* Standardized moment / (1:R)
* Stieltjes moment problem / anl (1:R)
* Trigonometric moment problem / anl (1:R)
* Uncorrelated / (2:R)
* Variance / (12F:DCR)
* Variance-to-mean ratio / (1:R)

### Inequalities (inq)

* Chebyshev's inequality / (1:R)
* An inequality on location and scale parameters / (1:R)
* Azuma's inequality / (F:BR)
* Bennett's inequality / (F:R)
* Bernstein inequalities / (F:R)
* Bhatia–Davis inequality
* Chernoff bound / (F:B)
* Doob's martingale inequality / (FU:R)
* Dudley's theorem / Gau
* Entropy power inequality
* Etemadi's inequality / (F:R)
* Gauss's inequality
* Hoeffding's inequality / (F:R)
* Khintchine inequality / (F:B)
* Kolmogorov's inequality / (F:R)
* Marcinkiewicz–Zygmund inequality / mnt
* Markov's inequality / (1:R)
* McDiarmid's inequality
* Multidimensional Chebyshev's inequality
* Paley–Zygmund inequality / (1:R)
* Pinsker's inequality / (2:R)
* Vysochanskiï–Petunin inequality / (1:C)

### Markov chains, processes, fields, networks (Mar)

* Markov chain / (FLSU:D)
* Additive Markov chain
* Bayesian network / Bay
* Birth–death process / (U:D)
* CIR process / scl
* Chapman–Kolmogorov equation / (F:DC)
* Cheeger bound / (L:D)
* Conductance
* Contact process
* Continuous-time Markov process / (U:D)
* Detailed balance / (F:D)
* Examples of Markov chains / (FL:D)
* Feller process / (U:G)
* Fokker–Planck equation / scl anl
* Foster's theorem / (L:D)
* Gauss–Markov process / Gau
* Geometric Brownian motion / scl
* Hammersley–Clifford theorem / (F:C)
* Harris chain / (L:DC)
* Hidden Markov model / (F:D)
* Hidden Markov random field
* Hunt process / (U:R)
* Kalman filter / (F:C)
* Kolmogorov backward equation / scl
* Kolmogorov's criterion / (F:D)
* Kolmogorov’s generalized criterion / (U:D)
* Krylov–Bogolyubov theorem / anl
* Lumpability
* Markov additive process
* Markov blanket / Bay
* Markov chain mixing time / (L:D)
* Markov decision process
* Markov information source
* Markov kernel
* Markov logic network
* Markov network
* Markov process / (U:D)
* Markov property / (F:D)
* Markov random field
* Master equation / phs (U:D)
* Milstein method / scl
* Moran process
* Ornstein–Uhlenbeck process / Gau scl
* Partially observable Markov decision process
* Product-form solution / spr
* Quantum Markov chain / phs
* Semi-Markov process
* Stochastic matrix / anl
* Telegraph process / (U:B)
* Variable-order Markov model
* Wiener process / Gau scl

### Gaussian random variables, vectors, functions (Gau)

* Normal distribution / spd
* Abstract Wiener space
* Brownian bridge
* Classical Wiener space
* Concentration dimension
* Dudley's theorem / inq
* Estimation of covariance matrices
* Fractional Brownian motion
* Gaussian isoperimetric inequality
* Gaussian measure / anl
* Gaussian random field
* Gauss–Markov process / Mar
* Integration of the normal density function / spd anl
* Gaussian process
* Isserlis Gaussian moment theorem / mnt
* Karhunen–Loève theorem
* Large deviations of Gaussian random functions / lrd
* Lévy's modulus of continuity theorem / (U:R)
* Matrix normal distribution / spd
* Multivariate normal distribution / spd
* Ornstein–Uhlenbeck process / Mar scl
* Paley–Wiener integral / anl
* Pregaussian class
* Schilder's theorem / lrd
* Wiener process / Mar scl

### Conditioning (cnd)

* Conditioning / (2:BDCR)
* Bayes' theorem / (2:BCG)
* Borel–Kolmogorov paradox / iex (2:CM)
* Conditional expectation / (2:BDR)
* Conditional independence / (3F:BR)
* Conditional probability
* Conditional probability distribution / (2:DC)
* Conditional random field / (F:R)
* Disintegration theorem / anl (2:G)
* Inverse probability / Bay
* Luce's choice axiom
* Regular conditional probability / (2:G)
* Rule of succession / (F:B)

### Specific distributions (spd)

* Binomial distribution / (1:D)
* (a,b,0) class of distributions / (1:D)
* Anscombe transform
* Bernoulli distribution / (1:B)
* Beta distribution / (1:C)
* Bose-Einstein statistics / (F:D)
* Cantor distribution / (1:C)
* Cauchy distribution / (1:C)
* Chi-squared distribution / (1:C)
* Compound Poisson distribution / (F:DR)
* Degenerate distribution / (1:D)
* Dirichlet distribution / (F:C)
* Discrete phase-type distribution / (1:D)
* Erlang distribution / (1:C)
* Exponential-logarithmic distribution / (1:C)
* Exponential distribution / (1:C)
* F-distribution / (1:C)
* Fermi-Dirac statistics / (1F:D)
* Fisher-Tippett distribution / (1:C)
* Gamma distribution / (1:C)
* Generalized normal distribution / (1:C)
* Geometric distribution / (1:D)
* Half circle distribution / (1:C)
* Hypergeometric distribution / (1:D)
* Normal distribution / Gau
* Integration of the normal density function / Gau anl
* Lévy distribution / (1:C)
* Matrix normal distribution / Gau
* Maxwell-Boltzmann statistics / (F:D)
* McCullagh's parametrization of the Cauchy distributions / (1:C)
* Multinomial distribution / (F:D)
* Multivariate normal distribution / Gau
* Negative binomial distribution / (1:D)
* Pareto distribution / (1:C)
* Phase-type distribution / (1:C)
* Poisson distribution / (1:D)
* Power law / (1:C)
* Skew normal distribution / (1:C)
* Stable distribution / (1:C)
* Student's t-distribution / (1:C)
* Tracy–Widom distribution / rmt
* Triangular distribution / (1:C)
* Weibull distribution / (1:C)
* Wigner semicircle distribution / (1:C)
* Wishart distribution / (F:C)
* Zeta distribution / (1:D)
* Zipf's law / (1:D)

### Empirical measure (emm)

* Donsker's theorem / (LU:C)
* Empirical distribution function
* Empirical measure / (FL:RG) (U:D)
* Empirical process / (FL:RG) (U:D)
* Glivenko–Cantelli theorem / (FL:RG) (U:D)
* Khmaladze transformation / (FL:RG) (U:D)
* Vapnik–Chervonenkis theory

### Limit theorems (lmt)

* Central limit theorem / (L:R)
* Berry–Esseen theorem / (F:R)
* Characteristic function / anl (1F:DCR)
* De Moivre–Laplace theorem / (L:BD)
* Helly–Bray theorem / anl (L:R)
* Illustration of the central limit theorem / (L:DC)
* Lindeberg's condition
* Lyapunov's central limit theorem / (L:R)
* Lévy's continuity theorem / anl (L:R)
* Lévy's convergence theorem / (S:R)
* Martingale central limit theorem / (S:R)
* Method of moments / mnt (L:R)
* Slutsky's theorem / anl
* Weak convergence of measures / anl

### Large deviations (lrd)

* Large deviations theory
* Contraction principle
* Cramér's theorem
* Exponentially equivalent measures
* Freidlin–Wentzell theorem
* Laplace principle
* Large deviations of Gaussian random functions / Gau
* Rate function
* Schilder's theorem / Gau
* Tilted large deviation principle
* Varadhan's lemma

### Random graphs (rgr)

* Random graph
* BA model
* Barabási–Albert model
* Erdős–Rényi model
* Percolation theory / phs (L:B)
* Percolation threshold / phs
* Random geometric graph
* Random regular graph
* Watts and Strogatz model

### Random matrices (rmt)

* Random matrix
* Circular ensemble
* Gaussian matrix ensemble
* Tracy–Widom distribution / spd
* Weingarten function / anl

### Stochastic calculus (scl)

* Itô calculus
* Bessel process
* CIR process / Mar
* Doléans-Dade exponential
* Dynkin's formula
* Euler–Maruyama method
* Feynman–Kac formula
* Filtering problem
* Fokker–Planck equation / Mar anl
* Geometric Brownian motion / Mar
* Girsanov theorem
* Green measure
* Heston model / fnc
* Hörmander's condition / anl
* Infinitesimal generator
* Itô's lemma
* Itô calculus
* Itô diffusion
* Itô isometry
* Itô's lemma
* Kolmogorov backward equation / Mar
* Local time
* Milstein method / Mar
* Novikov's condition
* Ornstein–Uhlenbeck process / Gau Mar
* Quadratic variation
* Random dynamical system / rds
* Reversible diffusion
* Runge–Kutta method
* Russo–Vallois integral
* Schramm–Loewner evolution
* Semimartingale
* Stochastic calculus
* Stochastic differential equation
* Stochastic processes and boundary value problems / anl
* Stratonovich integral
* Tanaka equation
* Tanaka's formula
* Wiener process / Gau Mar
* Wiener sausage

### Malliavin calculus (Mal)

* Malliavin calculus
* Clark–Ocone theorem
* H-derivative
* Integral representation theorem for classical Wiener space
* Integration by parts operator
* Malliavin derivative
* Malliavin's absolute continuity lemma
* Ornstein–Uhlenbeck operator
* Skorokhod integral

### Random dynamical systems (rds)

Random dynamical system / scl

* Absorbing set
* Base flow
* Pullback attractor

### Analytic aspects (including measure theoretic) (anl)

* Probability space
* Carleman's condition / mnt (1:R)
* Characteristic function / lmt (1F:DCR)
* Contiguity#Probability theory
* Càdlàg
* Disintegration theorem / cnd (2:G)
* Dynkin system
* Exponential family
* Factorial moment generating function / mnt (1:R)
* Filtration
* Fokker–Planck equation / scl Mar
* Gaussian measure / Gau
* Hamburger moment problem / mnt (1:R)
* Hausdorff moment problem / mnt (1:R)
* Helly–Bray theorem / lmt (L:R)
* Hörmander's condition / scl
* Integration of the normal density function / spd Gau
* Kolmogorov extension theorem / (SU:R)
* Krylov–Bogolyubov theorem / Mar
* Law (stochastic processes) / (U:G)
* Location-scale family
* Lévy's continuity theorem / lmt (L:R)
* Minlos' theorem
* Moment problem / mnt (1:R)
* Moment-generating function / mnt (1F:R)
* Natural filtration / (U:G)
* Paley–Wiener integral / Gau
* Sazonov's theorem
* Slutsky's theorem / lmt
* Standard probability space
* Stieltjes moment problem / mnt (1:R)
* Stochastic matrix / Mar
* Stochastic processes and boundary value problems / scl
* Trigonometric moment problem / mnt (1:R)
* Weak convergence of measures / lmt
* Weingarten function / rmt

## Core probability: other articles, by number and type of random variables

### A single random variable (1:)

#### Binary (1:B)

* Bernoulli trial / (1:B)
* Complementary event / (1:B)
* Entropy / (1:BDC)
* Event / (1:B)
* Indecomposable distribution / (1:BDCR)
* Indicator function / (1F:B)

#### Discrete (1:D)

* Binomial probability / (1:D)
* Continuity correction / (1:DC)
* Entropy / (1:BDC)
* Equiprobable / (1:D)
* Hann function / (1:D)
* Indecomposable distribution / (1:BDCR)
* Infinite divisibility / (1:DCR)
* Le Cam's theorem / (F:B) (1:D)
* Limiting density of discrete points / (1:DC)
* Mean difference / (1:DCR)
* Memorylessness / (1:DCR)
* Probability vector / (1:D)
* Probability-generating function / (1:D)
* Tsallis entropy / (1:DC)

#### Continuous (1:C)

* Almost surely / (1:C) (LS:D)
* Continuity correction / (1:DC)
* Edgeworth series / (1:C)
* Entropy / (1:BDC)
* Indecomposable distribution / (1:BDCR)
* Infinite divisibility / (1:DCR)
* Limiting density of discrete points / (1:DC)
* Location parameter / (1:C)
* Mean difference / (1:DCR)
* Memorylessness / (1:DCR)
* Monotone likelihood ratio / (1:C)
* Scale parameter / (1:C)
* Stability / (1:C)
* Stein's lemma / (12:C)
* Truncated distribution / (1:C)
* Tsallis entropy / (1:DC)

#### Real-valued, arbitrary (1:R)

* Heavy-tailed distribution / (1:R)
* Indecomposable distribution / (1:BDCR)
* Infinite divisibility / (1:DCR)
* Locality / (1:R)
* Mean difference / (1:DCR)
* Memorylessness / (1:DCR)
* Quantile / (1:R)
* Survival function / (1:R)
* Taylor expansions for the moments of functions of random variables / (1:R)

#### Random point of a manifold (1:M)

* Bertrand's paradox / (1:M)

#### General (random element of an abstract space) (1:G)

* Pitman–Yor process / (1:G)
* Random compact set / (1:G)
* Random element / (1:G)

### Two random variables (2:)

#### Binary (2:B)

* Coupling / (2:BRG)
* Craps principle / (2:B)

#### Discrete (2:D)

* Kullback–Leibler divergence / (2:DCR)
* Mutual information / (23F:DC)

#### Continuous (2:C)

* Copula / (2F:C)
* Cramér's theorem / (2:C)
* Kullback–Leibler divergence / (2:DCR)
* Mutual information / (23F:DC)
* Normally distributed and uncorrelated does not imply independent / (2:C)
* Posterior probability / Bay (2:C)
* Stein's lemma / (12:C)

#### Real-valued, arbitrary (2:R)

* Coupling / (2:BRG)
* Hellinger distance / (2:R)
* Kullback–Leibler divergence / (2:DCR)
* Lévy metric / (2:R)
* Total variation / (2:R)

#### General (random element of an abstract space) (2:G)

* Coupling / (2:BRG)
* Lévy–Prokhorov metric / (2:G)
* Wasserstein metric / (2:G)

### Three random variables (3:)

#### Binary (3:B)

* Pairwise independence / (3:B) (F:R)

#### Discrete (3:D)

* Mutual information / (23F:DC)

#### Continuous (3:C)

* Mutual information / (23F:DC)

### Finitely many random variables (F:)

#### Binary (F:B)

* Bertrand's ballot theorem / (F:B)
* Boole's inequality / (FS:B)
* Coin flipping / (F:B)
* Collectively exhaustive events / (F:B)
* Inclusion–exclusion principle / (F:B)
* Independence / (F:BR)
* Indicator function / (1F:B)
* Law of total probability / (F:B)
* Le Cam's theorem / (F:B) (1:D)
* Leftover hash lemma / (F:B)
* Lovász local lemma / (F:B)
* Mutually exclusive / (F:B)
* Random walk / (FLS:BD) (U:C)
* Schuette–Nesbitt formula / (F:B)

#### Discrete (F:D)

* Coupon collector's problem / gmb (F:D)
* Graphical model / (F:D)
* Kirkwood approximation / (F:D)
* Mutual information / (23F:DC)
* Random field / (F:D)
* Random walk / (FLS:BD) (U:C)
* Stopped process / (FU:DG)

#### Continuous (F:C)

* Anderson's theorem / (F:C)
* Autoregressive integrated moving average / (FS:C)
* Autoregressive model / (FS:C)
* Autoregressive moving average model / (FS:C)
* Copula / (2F:C)
* Maxwell's theorem / (F:C)
* Moving average model / (FS:C)
* Mutual information / (23F:DC)
* Schrödinger method / (F:C)

#### Real-valued, arbitrary (F:R)

* Bapat–Beg theorem / (F:R)
* Comonotonicity / (F:R)
* Doob martingale / (F:R)
* Independence / (F:BR)
* Littlewood–Offord problem / (F:R)
* Lévy flight / (F:R) (U:C)
* Martingale / (FU:R)
* Martingale difference sequence / (F:R)
* Maximum likelihood / (FL:R)
* Multivariate random variable / (F:R)
* Optional stopping theorem / (FS:R)
* Pairwise independence / (3:B) (F:R)
* Stopping time / (FU:R)
* Time series / (FS:R)
* Wald's equation / (FS:R)
* Wick product / (F:R)

#### General (random element of an abstract space) (F:G)

* Finite-dimensional distribution / (FU:G)
* Hitting time / (FU:G)
* Stopped process / (FU:DG)

### A large number of random variables (finite but tending to infinity) (L:)

#### Binary (L:B)

* Random walk / (FLS:BD) (U:C)

#### Discrete (L:D)

* Almost surely / (1:C) (LS:D)
* Gambler's ruin / gmb (L:D)
* Loop-erased random walk / (L:D) (U:C)
* Preferential attachment / (L:D)
* Random walk / (FLS:BD) (U:C)
* Typical set / (L:D)

#### Real-valued, arbitrary (L:R)

* Convergence of random variables / (LS:R)
* Law of large numbers / (LS:R)
* Maximum likelihood / (FL:R)
* Stochastic convergence / (LS:R)

### An infinite sequence of random variables (S:)

#### Binary (S:B)

* Bernoulli process / (S:B)
* Boole's inequality / (FS:B)
* Borel–Cantelli lemma / (S:B)
* De Finetti's theorem / (S:B)
* Exchangeable random variables / (S:BR)
* Random walk / (FLS:BD) (U:C)

#### Discrete (S:D)

* Almost surely / (1:C) (LS:D)
* Asymptotic equipartition property / (S:DC)
* Bernoulli scheme / (S:D)
* Branching process / (S:D)
* Chinese restaurant process / (S:D)
* Galton–Watson process / (S:D)
* Information source / (S:D)
* Random walk / (FLS:BD) (U:C)

#### Continuous (S:C)

* Asymptotic equipartition property / (S:DC)
* Autoregressive integrated moving average / (FS:C)
* Autoregressive model / (FS:C)
* Autoregressive–moving-average model / (FS:C)
* Moving-average model / (FS:C)

#### Real-valued, arbitrary (S:R)

* Big O in probability notation / (S:R)
* Convergence of random variables / (LS:R)
* Doob's martingale convergence theorems / (SU:R)
* Ergodic theory / (S:R)
* Exchangeable random variables / (S:BR)
* Hewitt–Savage zero–one law / (S:RG)
* Kolmogorov's zero–one law / (S:R)
* Law of large numbers / (LS:R)
* Law of the iterated logarithm / (S:R)
* Maximal ergodic theorem / (S:R)
* Op (statistics) / (S:R)
* Optional stopping theorem / (FS:R)
* Stationary process / (SU:R)
* Stochastic convergence / (LS:R)
* Stochastic process / (SU:RG)
* Time series / (FS:R)
* Uniform integrability / (S:R)
* Wald's equation / (FS:R)

#### General (random element of an abstract space) (S:G)

* Hewitt–Savage zero–one law / (S:RG)
* Mixing / (S:G)
* Skorokhod's representation theorem / (S:G)
* Stochastic process / (SU:RG)

### Uncountably many random variables (continuous-time processes etc) (U:)

#### Discrete (U:D)

* Counting process / (U:D)
* Cox process / (U:D)
* Dirichlet process / (U:D)
* Lévy process / (U:DC)
* Non-homogeneous Poisson process / (U:D)
* Point process / (U:D)
* Poisson process / (U:D)
* Poisson random measure / (U:D)
* Random measure / (U:D)
* Renewal theory / (U:D)
* Stopped process / (FU:DG)

#### Continuous (U:C)

* Brownian motion / phs (U:C)
* Gamma process / (U:C)
* Loop-erased random walk / (L:D) (U:C)
* Lévy flight / (F:R) (U:C)
* Lévy process / (U:DC)
* Martingale representation theorem / (U:C)
* Random walk / (FLS:BD) (U:C)
* Skorokhod's embedding theorem / (U:C)

#### Real-valued, arbitrary (U:R)

* Compound Poisson process / (U:R)
* Continuous stochastic process / (U:RG)
* Doob's martingale convergence theorems / (SU:R)
* Doob–Meyer decomposition theorem / (U:R)
* Feller-continuous process / (U:R)
* Kolmogorov continuity theorem / (U:R)
* Local martingale / (U:R)
* Martingale / (FU:R)
* Stationary process / (SU:R)
* Stochastic process / (SU:RG)
* Stopping time / (FU:R)

#### General (random element of an abstract space) (U:G)

* Adapted process / (U:G)
* Continuous stochastic process / (U:RG)
* Finite-dimensional distribution / (FU:G)
* Hitting time / (FU:G)
* Killed process / (U:G)
* Progressively measurable process / (U:G)
* Sample-continuous process / (U:G)
* Stochastic process / (SU:RG)
* Stopped process / (FU:DG)

## Around the core

### General aspects (grl)

* Aleatoric
* Average
* Bean machine
* Cox's theorem
* Equipossible
* Exotic probability
* Extractor
* Free probability
* Frequency
* Frequency probability
* Impossible event
* Infinite monkey theorem
* Information geometry
* Law of Truly Large Numbers
* Littlewood's law
* Observational error
* Principle of indifference
* Principle of maximum entropy
* Probability
* Probability interpretations
* Propensity probability
* Random number generator
* Random sequence
* Randomization
* Randomness
* Statistical dispersion
* Statistical regularity
* Uncertainty
* Upper and lower probabilities
* Urn problem

### Foundations of probability theory (fnd)

* Algebra of random variables
* Belief propagation
* Dempster–Shafer theory
* Dutch book
* Elementary event
* Normalizing constant
* Possibility theory
* Probability axioms
* Transferable belief model
* Unit measure

### Gambling (gmb)

* Betting
* Bookmaker
* Coherence
* Coupon collector's problem / (F:D)
* Coupon collector's problem (generating function approach) / (F:D)
* Gambler's fallacy
* Gambler's ruin / (L:D)
* Game of chance
* Inverse gambler's fallacy
* Lottery
* Lottery machine
* Luck
* Martingale
* Odds
* Pachinko
* Parimutuel betting
* Parrondo's paradox
* Pascal's wager
* Poker probability
* Poker probability (Omaha)
* Poker probability (Texas hold 'em)
* Pot odds
* Proebsting's paradox
* Roulette
* Spread betting
* The man who broke the bank at Monte Carlo

### Coincidence (cnc)

* Bible code
* Birthday paradox
* Birthday problem
* Index of coincidence
* Spurious relationship

### Algorithmics (alg)

* Algorithmic Lovász local lemma
* Box–Muller transform
* Gibbs sampling
* Inverse transform sampling method
* Las Vegas algorithm
* Metropolis algorithm
* Monte Carlo method
* Panjer recursion
* Probabilistic Turing machine
* Probabilistic algorithm
* Probabilistically checkable proof
* Probable prime
* Stochastic programming

### Bayesian approach (Bay)

* Bayes factor
* Bayesian model comparison
* Bayesian network / Mar
* Bayesian probability
* Bayesian programming
* Bayesianism
* Checking if a coin is fair
* Conjugate prior
* Factor graph
* Good–Turing frequency estimation
* Imprecise probability
* Inverse probability / cnd
* Marginal likelihood
* Markov blanket / Mar
* Posterior probability / (2:C)
* Prior probability
* SIPTA
* Subjective logic
* Subjectivism / hst

### Financial mathematics (fnc)

* Allais paradox
* Black–Scholes
* Cox–Ingersoll–Ross model
* Forward measure
* Heston model / scl
* Jump process
* Jump-diffusion model
* Kelly criterion
* Market risk
* Mathematics of bookmaking
* Risk
* Risk-neutral measure
* Ruin theory
* Sethi model
* Technical analysis
* Value at risk
* Variance gamma process / spr
* Vasicek model
* Volatility

### Physics (phs)

* Boltzmann factor
* Brownian motion / (U:C)
* Brownian ratchet
* Cosmic variance
* Critical phenomena
* Diffusion-limited aggregation
* Fluctuation theorem
* Gibbs state
* Information entropy
* Lattice model
* Master equation / Mar (U:D)
* Negative probability
* Nonextensive entropy
* Partition function
* Percolation theory / rgr (L:B)
* Percolation threshold / rgr
* Probability amplitude
* Quantum Markov chain / Mar
* Quantum probability
* Scaling limit
* Statistical mechanics
* Statistical physics
* Vacuum expectation value

### Genetics (gnt)

* Ewens's sampling formula
* Hardy–Weinberg principle
* Population genetics
* Punnett square
* Ronald Fisher

### Stochastic process (spr)

* Anomaly time series
* Arrival theorem
* Beverton–Holt model
* Burke's theorem
* Buzen's algorithm
* Disorder problem
* Erlang unit
* G-network
* Gordon–Newell theorem
* Innovation
* Interacting particle system
* Jump diffusion
* M/M/1 model
* M/M/c model
* Mark V Shaney
* Markov chain Monte Carlo
* Markov switching multifractal
* Oscillator linewidth
* Poisson hidden Markov model
* Population process
* Probabilistic cellular automata
* Product-form solution / Mar
* Quasireversibility
* Queueing theory
* Recurrence period density entropy
* Variance gamma process / fnc
* Wiener equation

### Geometric probability (geo)

* Boolean model
* Buffon's needle
* Geometric probability
* Hadwiger's theorem
* Integral geometry
* Random coil
* Stochastic geometry
* Vitale's random Brunn–Minkowski inequality

### Empirical findings (emp)

* Benford's law
* Pareto principle

### Historical (hst)

* History of probability
* Newton–Pepys problem
* Problem of points
* Subjectivism / Bay
* Sunrise problem
* The Doctrine of Chances

### Miscellany (msc)

* B-convex space
* Conditional event algebra
* Error function
* Goodman–Nguyen–van Fraassen algebra
* List of mathematical probabilists
* Nuisance variable
* Probabilistic encryption
* Probabilistic logic
* Probabilistic proofs of non-probabilistic theorems
* Pseudocount

## Counters of articles

* "Core": 455 (570)
* "Around": 198 (200)
* "Core selected": 311 (358)
* "Core others": 144 (212)

Here k(n) means: n links to k articles. (Some articles are linked more than once.)

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