Measure and integration Definition
Borel sigma-algebra
Builds on Sigma-algebraReal numbersInterval
Definition
Let be a topological space. The Borel σ-algebra is the smallest σ-algebra on that contains each open subset of . It is the intersection of all σ-algebras on that contain the open sets. A Borel set of is an element of .
Remarks
On the real line, is also the σ-algebra generated by the open intervals . The half-lines with also generate it. Each open set, each closed set, each countable set and each interval of is a Borel set. Some subsets of are not Borel sets. For example, the Vitali sets are not Lebesgue measurable, so they are not Borel sets.
Reading path
To understand this page from the ground up, read these 24 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
- Rational numbers object
- Real numbers object
- Set definition
- Ring of integers statement
- Disjunction definition
- Implication definition
- Predicate definition
- Union 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
- Borel sigma-algebra definition
Where this leads
328 pages build on this one.
- Variance
- Chernoff bound
- Chapman–Kolmogorov equation
- Bias of an estimator
- Identically distributed random variables
- Law of the unconscious statistician
- Density of the normal distribution
- Exponential distribution
- Covariance matrix
- Chebyshev's inequality
- Characteristic function of a sum of independent random variables
- Stationary distribution
- Bernoulli process
- Markov chain
- Statistic
- Law of total variance
- Variance of the normal distribution
- Cauchy–Schwarz inequality for expectations
- Correlation coefficient
- Variance under translation
- Almost sure convergence
- Law of total expectation
- Probability density function
- Expectation of a product of independent random variables
- Likelihood function
- Markov's inequality
- Standard score
- Poisson point process
- Gamma distribution
- De Moivre–Laplace theorem
- Kolmogorov's zero–one law
- Independent random variables are uncorrelated
- Convolution of probability distributions
- Normal distribution
- Mean of the Poisson distribution
- Statistical hypothesis test
- Independence means the joint law is the product of the marginals
- Median
- Method of moments
- Mean of the normal distribution
- Independent and identically distributed random variables
- Marginal distribution
- Moment-generating function of the normal distribution
- Quantile function
- Weak law of large numbers
- Random walk
- Random variable with a density
- Uncorrelated random variables that are not independent
- Kurtosis
- Martingale
- Variance of a sum
- Mean of the geometric distribution
- Expected value
- Strong law of large numbers
- Covariance
- Random variable
- Independence of two random variables
- Neyman–Pearson lemma
- Convergence in probability
- P-value
- Chi-squared test
- Convergence in probability implies convergence in distribution
- Central limit theorem
- 68–95–99.7 rule
- Transition matrix
- Conditional variance
- Lévy's continuity theorem
- Convergence of random variables
- Maximum likelihood estimation
- Multivariate normal distribution
- Skewness
- Galton–Watson process
- Markov property
- Student's t-test
- Discrete random variable
- Bernoulli distribution
- Convergence in distribution
- The cumulative distribution function determines the distribution
- Sample variance
- Bayesian inference
- Confidence interval
- Variance under scaling
- Discrete uniform distribution
- Coupon collector's problem
- Cumulative distribution function
- Characteristic function
- Cauchy distribution
- Variance as the mean of the square minus the square of the mean
- Moment-generating function
- Student's t-distribution
- Characteristic function of the normal distribution
- Sum of independent normal random variables
- Linearity of expectation
- Moment
- Beta distribution
- Expectation with respect to the binomial distribution
- Cumulant generating function
- Joint probability distribution
- Probability distribution
- Stochastic process
- Sample mean
- Type I and type II errors
- Generating function of a sum of independent random variables
- Chi-squared distribution
- Limit of the cumulative distribution function at infinity
- Hypergeometric distribution
- Moments are derivatives of the moment-generating function at zero
- Almost sure convergence implies convergence in probability
- The characteristic function determines the distribution
- Law of a random variable
- Convergence in mean
- Estimator
- Variance of a sum of independent random variables
- Mean squared error
- Poisson distribution
- Binomial distribution
- Standard deviation
- Probability mass function
- Gambler's ruin
- Continuous uniform distribution
- Hoeffding's inequality
- Moment-generating function of a sum of independent random variables
- Independence of random variables
- Geometric distribution
- Monotonicity of the cumulative distribution function
- Consistent estimator
- Poisson limit theorem
- Negative binomial distribution
- Tail sum formula for expectation
- Conditional expectation
- Absorbing Markov chain
- Sum of independent Poisson random variables
- Conditional probability distribution
- Probability-generating function
- Memorylessness
- Central moment
- Law of large numbers
- Doob's upcrossing inequality
- Bernstein–von Mises theorem
- Cramér–Rao bound
- Efficiency of an estimator
- Log-rank test
- Functional data analysis
- Poisson regression
- Sequential analysis
- Ergodic decomposition
- Koksma–Hlawka inequality
- Hodges' superefficient estimator
- Martingale representation theorem
- Mathematical statistics
- F-test
- Stationary process
- Cramér–Wold theorem
- Boolean model
- Stochastic partial differential equation
- Wald–Wolfowitz optimality of the sequential probability ratio test
- Doob's martingale inequality
- Sequential probability ratio test
- Brownian bridge
- Consistency of the maximum likelihood estimator
- Wiener process
- Empirical Bayes method
- Karhunen–Loève theorem
- Itô isometry
- Bayes factor
- Pickands–Balkema–De Haan theorem
- Basu's theorem
- Variogram
- Robbins–Monro algorithm
- Contiguity of measures
- Order statistic
- Lindley's paradox
- M-estimator
- Ornstein–Uhlenbeck process
- Kernel regression
- Adapted process
- 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
- Kriging
- Baire function
- Minimal sufficient statistic
- Whittle likelihood
- Logistic regression
- Convergence to equilibrium of ergodic Markov chains
- Cramér–von Mises criterion
- Azuma's inequality
- Gaussian free field
- Kernel density estimation
- Erdős–Kac theorem
- Detailed balance
- Coupling from the past
- Robust statistics
- Hidden Markov model
- Finite-dimensional distributions
- Continuous mapping theorem
- Bias–variance decomposition
- Lévy process
- Wald's equation
- Random ordinary differential equation
- Monte Carlo integration
- Time series
- Optional stopping theorem
- Measurable space
- Breakdown point
- Delta method
- Minimum description length
- Noncentral chi-squared distribution
- Hájek–Le Cam convolution theorem
- Harmonic measure
- Behrens–Fisher problem
- Submartingale
- Hodges–Lehmann estimator
- Kruskal–Wallis test
- Stochastic difference equation
- Sampling distribution
- Information geometry
- Recurrence and transience of states
- Malliavin calculus
- Wishart distribution
- Birth–death process
- Statistical inference
- Minimum-variance unbiased estimator
- Sign test
- Slutsky's theorem
- Tanaka's formula
- Geometric Brownian motion
- Empirical likelihood
- Dvoretzky–Kiefer–Wolfowitz inequality
- Bayesian information criterion
- Berry–Esseen theorem
- Hunt process
- Cochran's theorem
- Stochastic approximation
- Jackknife
- Quantile regression
- Markov chain mixing time
- Birkhoff's ergodic theorem
- Akaike information criterion
- Hewitt–Savage zero–one law
- Reflection principle
- Credible interval
- Conformal modulus
- Independence of sample mean and sample variance for normal samples
- Self-similar process
- Gibbs sampling
- Estimating equations
- Supermartingale
- Anderson–Darling test
- Spearman's rank correlation coefficient
- Family-wise error rate
- Markov chain Monte Carlo
- Dickey–Fuller test
- Generalized method of moments
- Doob's martingale convergence theorems
- Random integral equation
- Uniformly most powerful test
- Bernstein inequalities
- Kendall rank correlation coefficient
- Semimartingale
- Empirical distribution function
- Bootstrap
- Influence function
- Oseledets theorem
- Itô calculus
- Ancillary statistic
- Stopped process
- Moments of the log-normal distribution
- Complete statistic
- Horvitz–Thompson estimator
- U-statistic
- Fractional Brownian motion
- L-estimator
- Power of a test
- Randomization
- Sufficient statistic
- Gaussian process
- 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
- Extremal length
- Pivotal quantity
- Generalized linear model
- Box–Jenkins method
- Kolmogorov continuity theorem
- Pesin theory
- Empirical process
- Binomial proportion confidence interval
- Lévy's upward theorem
- False discovery rate and the Benjamini–Hochberg procedure
- Neyman construction
- Wilks' theorem
- Skorokhod's embedding theorem
- Permutation test
- Fisher's exact test
- Bonferroni correction
- Doob decomposition theorem
- Local asymptotic normality
- Frequentist inference
- Likelihood principle
- Fisher–Neyman factorization theorem
- Girsanov theorem
- Quadratic variation
- Observed information
- Feller process
- Point estimation
- Local martingale
Further reading
- Web https://en.wikipedia.org/wiki/Borel_set, Generating the Borel algebra
About this page
| Tag | 0BVHZXWKCD |
|---|---|
| URI | m2://0BVHZXWKCD |
| Kind | definition |
| Writing | W1 stated |
| MSC | 28A05 |
| Defines | Borel sigma-algebra |
| Uses | Sigma-algebra, Real numbers, Interval |
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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)
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