Probability theory and stochastic processes Definition
Independence of two events
Definition
Let be a probability space and let be events. The events and are independent if
Remarks
If , then and are independent if and only if , with the conditional probability. If and are independent, then so are and , and , and and . An event of probability or is independent of each event. An event is independent of itself if and only if . Independence is not the same as disjointness. Disjoint events and with and are never independent.
Reading path
To understand this page from the ground up, read these 30 pages in order. The foundations come first.
Show the path as a graph
- 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
Where this leads
178 pages build on this one.
- Chapman–Kolmogorov equation
- Bias of an estimator
- Characteristic function of a sum of independent random variables
- Stationary distribution
- Bernoulli process
- Markov chain
- Statistic
- Expectation of a product of independent random variables
- Likelihood function
- Poisson point process
- De Moivre–Laplace theorem
- Kolmogorov's zero–one law
- Independent random variables are uncorrelated
- Convolution of probability distributions
- Statistical hypothesis test
- Independence means the joint law is the product of the marginals
- 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
- Uncorrelated random variables that are not independent
- Strong law of large numbers
- Independence of two random variables
- Neyman–Pearson lemma
- P-value
- Chi-squared test
- Central limit theorem
- Transition matrix
- Maximum likelihood estimation
- Mutual independence of events
- Galton–Watson process
- Markov property
- Student's t-test
- Sample variance
- Bayesian inference
- Confidence interval
- Coupon collector's problem
- Pairwise independence
- 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
- Absorbing Markov chain
- Sum of independent Poisson random variables
- Conditional independence
- 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
- Metropolis–Hastings algorithm
- Continuous-time Markov chain
- Minimal sufficient statistic
- Whittle likelihood
- Logistic regression
- Convergence to equilibrium of ergodic Markov chains
- Cramér–von Mises criterion
- Azuma's inequality
- Kernel density estimation
- Erdős–Kac theorem
- Detailed balance
- Coupling from the past
- Robust statistics
- Hidden Markov model
- 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
- Recurrence and transience of states
- Wishart distribution
- Birth–death process
- Statistical inference
- Minimum-variance unbiased estimator
- Sign test
- Empirical likelihood
- Dvoretzky–Kiefer–Wolfowitz inequality
- Bayesian information criterion
- Berry–Esseen theorem
- Cochran's theorem
- Jackknife
- Markov chain mixing time
- Birkhoff's ergodic theorem
- Akaike information criterion
- Hewitt–Savage zero–one law
- Credible interval
- Independence of sample mean and sample variance for normal samples
- Gibbs sampling
- Estimating equations
- Anderson–Darling test
- Family-wise error rate
- Markov chain Monte Carlo
- 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
About this page
| Tag | 0P0MZ4X682 |
|---|---|
| URI | m2://0P0MZ4X682 |
| Kind | definition |
| Writing | W1 stated |
| MSC | 60A05 |
| Defines | Independence of two events |
| Uses | Probability space, Event, Conditional probability |
Markdown · JSON · Edit on GitHub
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.