Discrete random variable
Definition
Let be a real random variable on a probability space . is discrete if there is a countable set with . A distribution on is discrete if for some countable set .
Remarks
Then for each Borel set . So the probability mass function determines the law of . The cumulative distribution function of a discrete random variable is a step function when the set of values has no limit points. It jumps by at each value . The Bernoulli, binomial, geometric and Poisson distributions are discrete.
Explanations
L1 human
A discrete random variable takes values that you can list, such as . The number of heads in ten coin tosses and the number of calls in an hour are discrete random variables.
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What depends on this
94 nodes use this node, directly or through other nodes.
- Variance 007QED3E0Y
- Chernoff bound 014PEBGDYF
- Chapman–Kolmogorov equation 01APZ8GDQH
- Bias of an estimator 01BD2TFFP4
- Law of the unconscious statistician 01ZWX875AT
- Covariance matrix 02CBHV4ZZQ
- Chebyshev's inequality 02HZ1BK3MW
- Characteristic function of a sum of independent random variables 02JGYG0T7M
- Stationary distribution 02KYGSCTCN
- Bernoulli process 02XPXR1FBK
- Markov chain 033TJBZDG0
- Law of total variance 03BY5XTNA4
- Variance of the normal distribution 03EESZDB9A
- Cauchy–Schwarz inequality for expectations 03PGHKAJG3
- Correlation coefficient 04CTY6XYWN
- Variance under translation 04CYN5G6RY
- Law of total expectation 04RRB6HM00
- Expectation of a product of independent random variables 05AW1G0WE1
- Likelihood function 05FSTXM92W
- Markov's inequality 05PWWHYBX5
- Poisson point process 05TG0SE4HT
- De Moivre–Laplace theorem 065X9S8CM3
- Independent random variables are uncorrelated 077GN9HDR6
- Convolution of probability distributions 07CHJ5YX28
- Mean of the Poisson distribution 086HEVXR9Z
- Method of moments 09J1H07VAH
- Mean of the normal distribution 09PR1V6RWR
- Moment-generating function of the normal distribution 0B1G4E21G7
- Weak law of large numbers 0BFDHF2H7N
- Uncorrelated random variables that are not independent 0C6J8BQXVR
- Kurtosis 0CBQVVNBMP
- Martingale 0CDVK9RQBH
- Variance of a sum 0D86GJEZ39
- Mean of the geometric distribution 0DB0R4N5XX
- Expected value 0DCT0QZVQ1
- Strong law of large numbers 0DFWHQ839T
- Covariance 0DJ8W3HP2D
- Neyman–Pearson lemma 0EE19CFEEF
- Chi-squared test 0EN0V3NY20
- Central limit theorem 0EPZC4Z1FC
- 68–95–99.7 rule 0EYQ03XCR3
- Transition matrix 0F1KBVY161
- Conditional variance 0F1SN93KXE
- Lévy's continuity theorem 0FPWF7TTMB
- Convergence of random variables 0FQ968YKA1
- Maximum likelihood estimation 0FREPFKNS7
- Skewness 0GQ3H7VW3V
- Galton–Watson process 0H631CZP5K
- Markov property 0HB8SXHPFA
- Student's t-test 0HYP606RYF
- Bernoulli distribution 0J97P5KZY0
- Sample variance 0JN9P4A944
- Bayesian inference 0JWRJX2RMP
- Confidence interval 0K4NMMJBG2
- Variance under scaling 0KVQ9D86BM
- Discrete uniform distribution 0KYP951QWH
- Coupon collector's problem 0MXCP7MXWJ
- Characteristic function 0MYDNRT7PP
- 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
- Expectation with respect to the binomial distribution 0PVCTCKD3C
- Cumulant generating function 0Q2QC81VTW
- Sample mean 0QNRBHWV35
- Generating function of a sum of independent random variables 0REMT01N36
- Chi-squared distribution 0RFMMBHFTV
- Hypergeometric distribution 0SPBPW3K61
- Moments are derivatives of the moment-generating function at zero 0STK8KEY9G
- The characteristic function determines the distribution 0T8BYAFEXX
- Convergence in mean 0TTA57SEJG
- 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
- Hoeffding's inequality 0WPDNXGSSN
- Moment-generating function of a sum of independent random variables 0WRZ170YEH
- Geometric distribution 0XCVV5Q500
- 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
- Memorylessness 0ZAZZ34WQS
- Central moment 0ZT24EC90K
- Law of large numbers 0ZVEAYVRC3
Sources
- Web https://en.wikipedia.org/wiki/Probability_distribution, Discrete probability distribution
What links here
Used by
- Expected value 0DCT0QZVQ1
- Probability mass function 0WF4EJP91W