Probability theory and stochastic processes Definition
Mutual independence of events
Definition
Let be a probability space, let be an index set, and let be an event for each . The events are mutually independent if for each finite nonempty subset ,
Remarks
For two events this is the independence of two events. For three events the definition asks for four equations: one for each of the pairs , , and one for the triple . Mutual independence implies pairwise independence. The converse is false: see pairwise independent events that are not mutually independent. If we replace some of the events by their complements, the family stays mutually independent.
Reading path
To understand this page from the ground up, read these 31 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
- Set definition
- Ring of integers statement
- Disjunction definition
- Implication definition
- Predicate definition
- Union definition
- Sample space definition
- The rationals form a field statement
- Universal quantifier definition
- Subset definition
- The rationals form an ordered field statement
- Power set definition
- Universal set definition
- Order on the reals definition
- Complement definition
- Interval definition
- Sigma-algebra definition
- Event definition
- Probability axioms concept
- Probability measure definition
- Probability space definition
- Conditional probability definition
- Independence of two events definition
- Mutual independence of events definition
Where this leads
152 pages build on this one.
- Bias of an estimator
- Characteristic function of a sum of independent random variables
- Bernoulli process
- Statistic
- Likelihood function
- Poisson point process
- De Moivre–Laplace theorem
- Kolmogorov's zero–one law
- Statistical hypothesis test
- Method of moments
- Independent sigma-algebras
- Independent and identically distributed random variables
- Pairwise independent events that are not mutually independent
- Weak law of large numbers
- Random walk
- Strong law of large numbers
- Neyman–Pearson lemma
- P-value
- Chi-squared test
- Central limit theorem
- Maximum likelihood estimation
- Galton–Watson process
- Student's t-test
- Sample variance
- Bayesian inference
- Confidence interval
- Coupon collector's problem
- Student's t-distribution
- Second Borel–Cantelli lemma
- Sum of independent normal random variables
- Cumulant generating function
- Sample mean
- Type I and type II errors
- Generating function of a sum of independent random variables
- Chi-squared 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
- Independence of random variables
- Consistent estimator
- Sum of independent Poisson random variables
- 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
- Boolean model
- Wald–Wolfowitz optimality of the sequential probability ratio test
- Sequential probability ratio test
- Consistency of the maximum likelihood estimator
- 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
- Kolmogorov automorphism
- 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
- Hewitt–Savage zero–one law
- 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
- 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/Independence_(probability_theory), More than two events
About this page
| Tag | 0GCVEV6BJA |
|---|---|
| URI | m2://0GCVEV6BJA |
| Kind | definition |
| Writing | W0 stub |
| MSC | 60A05 |
| Defines | Mutual independence of events |
| Uses | Independence of two events, Probability space, Event |
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