Borel sigma-algebra
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.
Explanations
L1 human
We want to give a probability to intervals such as "the waiting time is between and minutes". The Borel sets are all the sets that we can build from intervals with complements and countable unions, again and again. So they are the events that come from intervals.
What this node 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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What depends on this
137 nodes use this node, directly or through other nodes.
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- Probability density function 04YG690N5V
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- De Moivre–Laplace theorem 065X9S8CM3
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- Convolution of probability distributions 07CHJ5YX28
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- Mean of the Poisson distribution 086HEVXR9Z
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- Student's t-test 0HYP606RYF
- Discrete random variable 0J6Z6H5VQJ
- Bernoulli distribution 0J97P5KZY0
- Convergence in distribution 0JDAQHWA9N
- The cumulative distribution function determines the distribution 0JEVZAFDY6
- Sample variance 0JN9P4A944
- Bayesian inference 0JWRJX2RMP
- Confidence interval 0K4NMMJBG2
- Variance under scaling 0KVQ9D86BM
- Discrete uniform distribution 0KYP951QWH
- Coupon collector's problem 0MXCP7MXWJ
- Cumulative distribution function 0MXMS3AE13
- Characteristic function 0MYDNRT7PP
- Cauchy distribution 0NA6Q82YR7
- Variance as the mean of the square minus the square of the mean 0NMF95YK8W
- Moment-generating function 0NNRNGKMFM
- Student's t-distribution 0NQ5798SB1
- Characteristic function of the normal distribution 0NS0TDA4E2
- Sum of independent normal random variables 0NXGPJWNFN
- Linearity of expectation 0P7PTC8DDE
- Moment 0PH0VHTQS0
- Beta distribution 0PP5ZAJPP7
- Expectation with respect to the binomial distribution 0PVCTCKD3C
- Cumulant generating function 0Q2QC81VTW
- Joint probability distribution 0QAR0XDZH4
- Probability distribution 0QB913WM5E
- Stochastic process 0QH5ENKZM1
- Sample mean 0QNRBHWV35
- Type I and type II errors 0R41PVFA39
- Generating function of a sum of independent random variables 0REMT01N36
- Chi-squared distribution 0RFMMBHFTV
- Limit of the cumulative distribution function at infinity 0RJ5TZH6G0
- Hypergeometric distribution 0SPBPW3K61
- Moments are derivatives of the moment-generating function at zero 0STK8KEY9G
- Almost sure convergence implies convergence in probability 0SVN6X695K
- The characteristic function determines the distribution 0T8BYAFEXX
- Law of a random variable 0TST4MHA3A
- Convergence in mean 0TTA57SEJG
- Estimator 0TTNRP5PFQ
- Variance of a sum of independent random variables 0V0778DCW9
- Mean squared error 0V4VSCBXP3
- Poisson distribution 0VEFSSKX18
- Binomial distribution 0VRWZDWW6B
- Standard deviation 0VTN5RKNC5
- Probability mass function 0WF4EJP91W
- Gambler's ruin 0WFMKK7DFZ
- Continuous uniform distribution 0WGDPYR30Q
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- Moment-generating function of a sum of independent random variables 0WRZ170YEH
- Independence of random variables 0XCE070V8B
- Geometric distribution 0XCVV5Q500
- Monotonicity of the cumulative distribution function 0XD22PBMWA
- Consistent estimator 0XJVF81QF6
- Poisson limit theorem 0XKTZRWTXX
- Negative binomial distribution 0XN7KEVSSS
- Tail sum formula for expectation 0Y6QNF1J49
- Conditional expectation 0YAMJRK04E
- Absorbing Markov chain 0YB93GWTKK
- Sum of independent Poisson random variables 0YH1M56SBN
- Conditional probability distribution 0YVDC5PYDW
- Probability-generating function 0Z8PSSAVBA
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Sources
- Web https://en.wikipedia.org/wiki/Borel_set, Generating the Borel algebra
What links here
Used by
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