Probability theory and stochastic processes Definition
Conditional probability
Builds on Probability spaceEvent
Definition
Let be a probability space and let be events with . The conditional probability of given is
Remarks
For fixed with , the map is a probability measure on . It gives all its mass to . For one roll of a fair die, . In general . Bayes' theorem relates the two. The definition does not apply when . Conditioning on such events needs the conditional expectation.
Reading path
To understand this page from the ground up, read these 29 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
Where this leads
219 pages build on this one.
- Prior probability
- Chapman–Kolmogorov equation
- Bias of an estimator
- Characteristic function of a sum of independent random variables
- Stationary distribution
- Bernoulli process
- Markov chain
- Statistic
- Law of total variance
- Law of total expectation
- 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
- Multiplication rule
- Random walk
- Uncorrelated random variables that are not independent
- Martingale
- Strong law of large numbers
- Base rate fallacy
- Law of total probability
- Independence of two random variables
- Neyman–Pearson lemma
- P-value
- Chi-squared test
- Central limit theorem
- Transition matrix
- Conditional variance
- Maximum likelihood estimation
- Mutual independence of events
- Galton–Watson process
- Markov property
- Student's t-test
- Sample variance
- Bayesian inference
- Confidence interval
- Posterior probability
- Coupon collector's problem
- Pairwise independence
- Bayes' theorem
- Student's t-distribution
- Second Borel–Cantelli lemma
- Sum of independent normal random variables
- Independence of two events
- Monty Hall problem
- 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
- Law of total probability for an event and its complement
- Gambler's ruin
- Hoeffding's inequality
- Moment-generating function of a sum of independent random variables
- Independence of random variables
- Consistent estimator
- Conditional expectation
- Absorbing Markov chain
- Sum of independent Poisson random variables
- Conditional probability distribution
- Memorylessness
- Conditional independence
- Law of large numbers
- Doob's upcrossing inequality
- 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
- Doob's martingale inequality
- 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
- Bayes estimator
- 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
- 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
- Wald's equation
- Monte Carlo integration
- 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
- Sampling distribution
- Recurrence and transience of states
- Wishart distribution
- Birth–death process
- Bayes risk
- Statistical inference
- Minimum-variance unbiased estimator
- Sign test
- Tanaka's formula
- Empirical likelihood
- Dvoretzky–Kiefer–Wolfowitz inequality
- Bayesian information criterion
- Berry–Esseen theorem
- Cochran's theorem
- Stochastic approximation
- 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
- Supermartingale
- Anderson–Darling test
- Family-wise error rate
- Markov chain Monte Carlo
- Dickey–Fuller test
- Generalized method of moments
- Doob's martingale convergence theorems
- Uniformly most powerful test
- Semimartingale
- Empirical distribution function
- Bootstrap
- Oseledets theorem
- Ancillary statistic
- Jeffreys prior
- Complete statistic
- Horvitz–Thompson estimator
- U-statistic
- Bayesian hierarchical model
- 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
- Burkholder–Davis–Gundy inequality
- Doob–Meyer decomposition theorem
- Cross-validation
- Wald test
- Generalized linear model
- Box–Jenkins method
- Shrinkage estimator
- Pesin theory
- Binomial proportion confidence interval
- Lévy's upward theorem
- False discovery rate and the Benjamini–Hochberg procedure
- Particle filter
- Neyman construction
- Wilks' theorem
- Skorokhod's embedding theorem
- Permutation test
- Fisher's exact test
- Bonferroni correction
- Doob decomposition theorem
- Frequentist inference
- Likelihood principle
- Fisher–Neyman factorization theorem
- Girsanov theorem
- Observed information
- Point estimation
- Local martingale
Further reading
- Web https://en.wikipedia.org/wiki/Conditional_probability, Kolmogorov definition
About this page
| Tag | 0FJB06AD2P |
|---|---|
| URI | m2://0FJB06AD2P |
| Kind | definition |
| Writing | W1 stated |
| MSC | 60A05 |
| Defines | Conditional probability |
| Uses | Probability space, Event |
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