Probability theory and stochastic processes Definition
Law of a random variable
Builds on Random variableProbability distribution
Definition
Let be a probability space, let be a set with a σ-algebra, and let be a random variable. The law of , also called the distribution of , is the function with
Remarks
is a probability distribution on . It is the image measure, or pushforward, of under , and one also writes or . Notation: means that the law of is . Two random variables can have the same law but be different functions. For a fair coin toss, the indicator of heads and the indicator of tails both have the Bernoulli law with parameter .
Reading path
To understand this page from the ground up, read these 40 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 distribution definition
- Probability space definition
- Random variable definition
- Law of a random variable definition
Where this leads
167 pages build on this one.
- Chernoff bound
- Bias of an estimator
- Identically distributed random variables
- Law of the unconscious statistician
- Characteristic function of a sum of independent random variables
- Bernoulli process
- Statistic
- Expectation of a product of independent random variables
- Likelihood function
- De Moivre–Laplace theorem
- Independent random variables are uncorrelated
- Statistical hypothesis test
- Independence means the joint law is the product of the marginals
- Method of moments
- Independent and identically distributed random variables
- Marginal distribution
- Moment-generating function of the normal distribution
- Weak law of large numbers
- Random walk
- Uncorrelated random variables that are not independent
- Kurtosis
- Strong law of large numbers
- Neyman–Pearson lemma
- P-value
- Chi-squared test
- Central limit theorem
- Lévy's continuity theorem
- Maximum likelihood estimation
- Skewness
- Galton–Watson process
- Student's t-test
- Sample variance
- Bayesian inference
- Confidence interval
- Coupon collector's problem
- Characteristic function
- Moment-generating function
- Student's t-distribution
- Characteristic function of the normal distribution
- Sum of independent normal random variables
- Moment
- Cumulant generating function
- Joint probability distribution
- Sample mean
- Type I and type II errors
- Generating function of a sum of independent random variables
- Chi-squared distribution
- Moments are derivatives of the moment-generating function at zero
- The characteristic function determines the distribution
- Estimator
- Variance of a sum of independent random variables
- Mean squared error
- Gambler's ruin
- Hoeffding's inequality
- Moment-generating function of a sum of independent random variables
- Consistent estimator
- Sum of independent Poisson random variables
- Conditional probability distribution
- Central moment
- Law of large numbers
- Bernstein–von Mises theorem
- Efficiency of an estimator
- Log-rank test
- Poisson regression
- Sequential analysis
- Ergodic decomposition
- Koksma–Hlawka inequality
- Hodges' superefficient estimator
- Mathematical statistics
- F-test
- Cramér–Wold theorem
- Wald–Wolfowitz optimality of the sequential probability ratio test
- Sequential probability ratio test
- Consistency of the maximum likelihood estimator
- Empirical Bayes method
- Bayes factor
- Pickands–Balkema–De Haan theorem
- Basu's theorem
- Order statistic
- Lindley's paradox
- M-estimator
- Kernel regression
- Score test
- Kingman's subadditive ergodic theorem
- Multivariate analysis of variance
- McDiarmid's inequality
- Asymptotic normality of the maximum likelihood estimator
- Mann–Whitney U test
- Score
- F-distribution
- Edgeworth expansion
- Monotone likelihood ratio
- Sample mean and sample covariance
- Wilcoxon signed-rank test
- Minimal sufficient statistic
- Whittle likelihood
- Logistic regression
- Cramér–von Mises criterion
- Azuma's inequality
- Kernel density estimation
- Erdős–Kac theorem
- Robust statistics
- Bias–variance decomposition
- Monte Carlo integration
- Breakdown point
- Delta method
- Noncentral chi-squared distribution
- Hájek–Le Cam convolution theorem
- Behrens–Fisher problem
- Hodges–Lehmann estimator
- Kruskal–Wallis test
- Sampling distribution
- Wishart distribution
- Statistical inference
- Minimum-variance unbiased estimator
- Sign test
- Empirical likelihood
- Dvoretzky–Kiefer–Wolfowitz inequality
- Bayesian information criterion
- Berry–Esseen theorem
- Cochran's theorem
- Jackknife
- Birkhoff's ergodic theorem
- Akaike information criterion
- Credible interval
- Independence of sample mean and sample variance for normal samples
- Estimating equations
- Anderson–Darling test
- Family-wise error rate
- Dickey–Fuller test
- Generalized method of moments
- Uniformly most powerful test
- Bernstein inequalities
- Empirical distribution function
- Bootstrap
- Oseledets theorem
- Ancillary statistic
- Complete statistic
- Horvitz–Thompson estimator
- U-statistic
- L-estimator
- Power of a test
- Sufficient statistic
- Missing data mechanisms
- Likelihood-ratio test
- Karlin–Rubin theorem
- Conjugate prior
- Kolmogorov–Smirnov test
- Expectation–maximization algorithm
- Hotelling's T-squared distribution
- Cross-validation
- Wald test
- Generalized linear model
- Box–Jenkins method
- Pesin theory
- Binomial proportion confidence interval
- False discovery rate and the Benjamini–Hochberg procedure
- Neyman construction
- Wilks' theorem
- Permutation test
- Fisher's exact test
- Bonferroni correction
- Frequentist inference
- Likelihood principle
- Fisher–Neyman factorization theorem
- Observed information
- Point estimation
Further reading
- Web https://en.wikipedia.org/wiki/Probability_distribution, General probability definition
About this page
| Tag | 0TST4MHA3A |
|---|---|
| URI | m2://0TST4MHA3A |
| Kind | definition |
| Writing | W0 stub |
| MSC | 60E05 |
| Defines | Law of a random variable |
| Uses | Random variable, Probability distribution |
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