Probability theory and stochastic processes Definition
Random variable
Builds on Probability spaceSigma-algebraBorel sigma-algebraFunctionPreimage of a set
Definition
Let be a probability space and let be a set with a σ-algebra . A random variable with values in is a function such that the preimage is in for each . A real random variable is a random variable with values in , with the Borel σ-algebra on .
Remarks
The condition says that is a measurable function. It makes an event, so is defined. A real function on is a random variable if and only if for each . For two rolls of a die, and the sum is a random variable. Sums, products and continuous functions of real random variables are random variables.
Reading path
To understand this page from the ground up, read these 38 pages in order. The foundations come first.
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- no evidence level
- Construction of the reals from Cauchy sequences concept
- Element definition
- Integers object
- Ordered field definition
- Proposition concept
- Random experiment concept
- Rational numbers object
- Real numbers object
- Set definition
- Ring of integers statement
- Disjunction definition
- Implication definition
- Predicate definition
- Ordered pair definition
- Union definition
- Sample space definition
- The rationals form a field statement
- Existential quantifier definition
- Universal quantifier definition
- Cartesian product definition
- Subset definition
- The rationals form an ordered field statement
- Uniqueness quantifier definition
- Binary relation definition
- Universal set definition
- Order on the reals definition
- Power set definition
- Complement definition
- Function definition
- Interval definition
- Preimage of a set definition
- Sigma-algebra definition
- Event definition
- Borel sigma-algebra definition
- Probability axioms concept
- Probability measure definition
- Probability space definition
- Random variable definition
Where this leads
319 pages build on this one.
- Variance
- Chernoff bound
- Chapman–Kolmogorov equation
- Bias of an estimator
- Identically distributed random variables
- Law of the unconscious statistician
- Density of the normal distribution
- Exponential distribution
- Covariance matrix
- Chebyshev's inequality
- Characteristic function of a sum of independent random variables
- Stationary distribution
- Bernoulli process
- Markov chain
- Statistic
- Law of total variance
- Variance of the normal distribution
- Cauchy–Schwarz inequality for expectations
- Correlation coefficient
- Variance under translation
- Almost sure convergence
- Law of total expectation
- Probability density function
- Expectation of a product of independent random variables
- Likelihood function
- Markov's inequality
- Standard score
- Poisson point process
- Gamma distribution
- De Moivre–Laplace theorem
- Kolmogorov's zero–one law
- Independent random variables are uncorrelated
- Convolution of probability distributions
- Normal distribution
- Mean of the Poisson distribution
- Statistical hypothesis test
- Independence means the joint law is the product of the marginals
- Median
- Method of moments
- Mean of the normal distribution
- Independent and identically distributed random variables
- Marginal distribution
- Moment-generating function of the normal distribution
- Quantile function
- Weak law of large numbers
- Random walk
- Random variable with a density
- Uncorrelated random variables that are not independent
- Kurtosis
- Martingale
- Variance of a sum
- Mean of the geometric distribution
- Expected value
- Strong law of large numbers
- Covariance
- Independence of two random variables
- Neyman–Pearson lemma
- Convergence in probability
- P-value
- Chi-squared test
- Convergence in probability implies convergence in distribution
- Central limit theorem
- 68–95–99.7 rule
- Transition matrix
- Conditional variance
- Lévy's continuity theorem
- Convergence of random variables
- Maximum likelihood estimation
- Multivariate normal distribution
- Skewness
- Galton–Watson process
- Markov property
- Student's t-test
- Discrete random variable
- Bernoulli distribution
- Convergence in distribution
- The cumulative distribution function determines the distribution
- Sample variance
- Bayesian inference
- Confidence interval
- Variance under scaling
- Discrete uniform distribution
- Coupon collector's problem
- Cumulative distribution function
- Characteristic function
- Cauchy distribution
- Variance as the mean of the square minus the square of the mean
- Moment-generating function
- Student's t-distribution
- Characteristic function of the normal distribution
- Sum of independent normal random variables
- Linearity of expectation
- Moment
- Beta distribution
- Expectation with respect to the binomial distribution
- Cumulant generating function
- Joint probability distribution
- Stochastic process
- Sample mean
- Type I and type II errors
- Generating function of a sum of independent random variables
- Chi-squared distribution
- Limit of the cumulative distribution function at infinity
- Hypergeometric distribution
- Moments are derivatives of the moment-generating function at zero
- Almost sure convergence implies convergence in probability
- The characteristic function determines the distribution
- Law of a random variable
- Convergence in mean
- Estimator
- Variance of a sum of independent random variables
- Mean squared error
- Poisson distribution
- Binomial distribution
- Standard deviation
- Probability mass function
- Gambler's ruin
- Continuous uniform distribution
- Hoeffding's inequality
- Moment-generating function of a sum of independent random variables
- Independence of random variables
- Geometric distribution
- Monotonicity of the cumulative distribution function
- Consistent estimator
- Poisson limit theorem
- Negative binomial distribution
- Tail sum formula for expectation
- Conditional expectation
- Absorbing Markov chain
- Sum of independent Poisson random variables
- Conditional probability distribution
- Probability-generating function
- Memorylessness
- Central moment
- Law of large numbers
- Doob's upcrossing inequality
- Bernstein–von Mises theorem
- Cramér–Rao bound
- Efficiency of an estimator
- Log-rank test
- Functional data analysis
- Poisson regression
- Sequential analysis
- Ergodic decomposition
- Koksma–Hlawka inequality
- Hodges' superefficient estimator
- Martingale representation theorem
- Mathematical statistics
- F-test
- Stationary process
- Cramér–Wold theorem
- Boolean model
- Stochastic partial differential equation
- Wald–Wolfowitz optimality of the sequential probability ratio test
- Doob's martingale inequality
- Sequential probability ratio test
- Brownian bridge
- Consistency of the maximum likelihood estimator
- Wiener process
- Empirical Bayes method
- Karhunen–Loève theorem
- Itô isometry
- Bayes factor
- Pickands–Balkema–De Haan theorem
- Basu's theorem
- Variogram
- Robbins–Monro algorithm
- Contiguity of measures
- Order statistic
- Lindley's paradox
- M-estimator
- Ornstein–Uhlenbeck process
- Kernel regression
- Adapted process
- Score test
- Kingman's subadditive ergodic theorem
- Multivariate analysis of variance
- McDiarmid's inequality
- Lévy's characterization of Brownian motion
- Asymptotic normality of the maximum likelihood estimator
- Mann–Whitney U test
- Kolmogorov automorphism
- Score
- F-distribution
- Edgeworth expansion
- Monotone likelihood ratio
- Sample mean and sample covariance
- Wilcoxon signed-rank test
- Metropolis–Hastings algorithm
- Continuous-time Markov chain
- Kriging
- Minimal sufficient statistic
- Whittle likelihood
- Logistic regression
- Convergence to equilibrium of ergodic Markov chains
- Cramér–von Mises criterion
- Azuma's inequality
- Gaussian free field
- Kernel density estimation
- Erdős–Kac theorem
- Detailed balance
- Coupling from the past
- Robust statistics
- Hidden Markov model
- Finite-dimensional distributions
- Continuous mapping theorem
- Bias–variance decomposition
- Lévy process
- Wald's equation
- Random ordinary differential equation
- Monte Carlo integration
- Time series
- Optional stopping theorem
- Breakdown point
- Delta method
- Noncentral chi-squared distribution
- Hájek–Le Cam convolution theorem
- Behrens–Fisher problem
- Submartingale
- Hodges–Lehmann estimator
- Kruskal–Wallis test
- Stochastic difference equation
- Sampling distribution
- Information geometry
- Recurrence and transience of states
- Malliavin calculus
- Wishart distribution
- Birth–death process
- Statistical inference
- Minimum-variance unbiased estimator
- Sign test
- Slutsky's theorem
- Tanaka's formula
- Geometric Brownian motion
- Empirical likelihood
- Dvoretzky–Kiefer–Wolfowitz inequality
- Bayesian information criterion
- Berry–Esseen theorem
- Hunt process
- Cochran's theorem
- Stochastic approximation
- Jackknife
- Quantile regression
- Markov chain mixing time
- Birkhoff's ergodic theorem
- Akaike information criterion
- Hewitt–Savage zero–one law
- Reflection principle
- Credible interval
- Independence of sample mean and sample variance for normal samples
- Self-similar process
- Gibbs sampling
- Estimating equations
- Supermartingale
- Anderson–Darling test
- Spearman's rank correlation coefficient
- Family-wise error rate
- Markov chain Monte Carlo
- Dickey–Fuller test
- Generalized method of moments
- Doob's martingale convergence theorems
- Random integral equation
- Uniformly most powerful test
- Bernstein inequalities
- Kendall rank correlation coefficient
- Semimartingale
- Empirical distribution function
- Bootstrap
- Influence function
- Oseledets theorem
- Itô calculus
- Ancillary statistic
- Stopped process
- Moments of the log-normal distribution
- Complete statistic
- Horvitz–Thompson estimator
- U-statistic
- Fractional Brownian motion
- L-estimator
- Power of a test
- Sufficient statistic
- Gaussian process
- Missing data mechanisms
- Likelihood-ratio test
- Karlin–Rubin theorem
- Conjugate prior
- Kolmogorov–Smirnov test
- Expectation–maximization algorithm
- Hotelling's T-squared distribution
- Burkholder–Davis–Gundy inequality
- Doob–Meyer decomposition theorem
- Cross-validation
- Wald test
- Pivotal quantity
- Generalized linear model
- Box–Jenkins method
- Kolmogorov continuity theorem
- Pesin theory
- Empirical process
- Binomial proportion confidence interval
- Lévy's upward theorem
- False discovery rate and the Benjamini–Hochberg procedure
- Neyman construction
- Wilks' theorem
- Skorokhod's embedding theorem
- Permutation test
- Fisher's exact test
- Bonferroni correction
- Doob decomposition theorem
- Local asymptotic normality
- Frequentist inference
- Likelihood principle
- Fisher–Neyman factorization theorem
- Girsanov theorem
- Quadratic variation
- Observed information
- Feller process
- Point estimation
- Local martingale
Further reading
- Web https://en.wikipedia.org/wiki/Random_variable, Measure-theoretic definition
About this page
| Tag | 0DKRR22AEQ |
|---|---|
| URI | m2://0DKRR22AEQ |
| Kind | definition |
| Writing | W1 stated |
| MSC | 60A05 |
| Defines | Random variable |
| Uses | Probability space, Sigma-algebra, Borel sigma-algebra, Function, Preimage of a set |
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What this page needs for W2
- edge instance_of in: one example (0 of 1)
- edge not_instance_of in: one non-example that fails one clause (0 of 1)
The writing levels say how complete the text of a page is. They do not measure its truth.
What links here
Used by
- Variance
- Statistic
- Almost sure convergence
- Standard score
- Median
- Random variable with a density
- Expected value
- Independence of two random variables
- Convergence in probability
- Multivariate normal distribution
- Discrete random variable
- Convergence in distribution
- Cumulative distribution function
- Joint probability distribution
- Stochastic process
- Law of a random variable
- Convergence in mean
- Probability mass function
- Independence of random variables
- Conditional expectation
- Sequential analysis
- Robbins–Monro algorithm
- Order statistic
- Stochastic approximation
- Kendall rank correlation coefficient
- Missing data mechanisms
- Pivotal quantity