Probability theory and stochastic processes Definition
Convergence in probability
Builds on Random variable
Definition
Let be real random variables on one probability space . The sequence converges in probability to if for each , One writes .
Remarks
The limit is unique up to equality almost surely. If in probability, then a subsequence of converges to almost surely. The weak law of large numbers is a result about convergence in probability. Example: let be with probability and otherwise. Then in probability.
Reading path
To understand this page from the ground up, read these 39 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
- Convergence in probability definition
Where this leads
36 pages build on this one.
- Method of moments
- Weak law of large numbers
- Strong law of large numbers
- Convergence in probability implies convergence in distribution
- Convergence of random variables
- Maximum likelihood estimation
- Almost sure convergence implies convergence in probability
- Consistent estimator
- Bernstein–von Mises theorem
- Poisson regression
- Ergodic decomposition
- Koksma–Hlawka inequality
- Consistency of the maximum likelihood estimator
- M-estimator
- Kingman's subadditive ergodic theorem
- Asymptotic normality of the maximum likelihood estimator
- Whittle likelihood
- Logistic regression
- Continuous mapping theorem
- Monte Carlo integration
- Slutsky's theorem
- Bayesian information criterion
- Birkhoff's ergodic theorem
- Akaike information criterion
- Estimating equations
- Generalized method of moments
- Oseledets theorem
- Likelihood-ratio test
- Expectation–maximization algorithm
- Wald test
- Generalized linear model
- Box–Jenkins method
- Pesin theory
- Local asymptotic normality
- Observed information
- Point estimation
Further reading
- Web https://en.wikipedia.org/wiki/Convergence_of_random_variables, Convergence in probability
About this page
| Tag | 0EFDDXTVD6 |
|---|---|
| URI | m2://0EFDDXTVD6 |
| Kind | definition |
| Writing | W1 stated |
| MSC | 60F05 |
| Defines | Convergence in probability |
| Uses | Random variable |
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