Probability theory and stochastic processes Definition
Random variable with a density
Builds on Random variableBorel sigma-algebra
Definition
Let be a real random variable on a probability space . has a density, or is absolutely continuous, if there is a Borel measurable function with the following property. For each Borel set , where the integral is the Lebesgue integral.
Remarks
Such a function is a probability density function of . If has a density, then for each , and the cumulative distribution function is continuous. A continuous cumulative distribution function does not imply a density. The Cantor distribution has a continuous cumulative distribution function and no density. The same definition with and the Lebesgue measure on gives random vectors with a density.
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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
- Random variable with a density definition
Where this leads
214 pages build on this one.
- Variance
- Chernoff bound
- Chapman–Kolmogorov equation
- Bias of an estimator
- 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
- Markov chain
- Law of total variance
- Variance of the normal distribution
- Cauchy–Schwarz inequality for expectations
- Correlation coefficient
- Variance under translation
- Law of total expectation
- Probability density function
- Expectation of a product of independent random variables
- Likelihood function
- Markov's inequality
- Poisson point process
- Gamma distribution
- De Moivre–Laplace theorem
- Independent random variables are uncorrelated
- Convolution of probability distributions
- Normal distribution
- Mean of the Poisson distribution
- Method of moments
- Mean of the normal distribution
- Moment-generating function of the normal distribution
- Weak law of large numbers
- 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
- Neyman–Pearson lemma
- P-value
- Chi-squared test
- 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
- Sample variance
- Bayesian inference
- Confidence interval
- Variance under scaling
- Coupon collector's problem
- 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
- Sample mean
- Generating function of a sum of independent random variables
- Chi-squared distribution
- Moments are derivatives of the moment-generating function at zero
- The characteristic function determines the distribution
- Convergence in mean
- Variance of a sum of independent random variables
- Mean squared error
- Standard deviation
- Continuous uniform distribution
- Hoeffding's inequality
- Moment-generating function of a sum of independent random variables
- 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
- Doob's upcrossing inequality
- Bernstein–von Mises theorem
- Cramér–Rao bound
- Efficiency of an estimator
- Poisson regression
- Ergodic decomposition
- Koksma–Hlawka inequality
- Hodges' superefficient estimator
- Martingale representation theorem
- Cramér–Wold theorem
- Boolean model
- Wald–Wolfowitz optimality of the sequential probability ratio test
- Doob's martingale inequality
- Consistency of the maximum likelihood estimator
- Itô isometry
- Bayes factor
- Basu's theorem
- Contiguity of measures
- Lindley's paradox
- M-estimator
- Score test
- Kingman's subadditive ergodic theorem
- McDiarmid's inequality
- Lévy's characterization of Brownian motion
- Asymptotic normality of the maximum likelihood estimator
- Score
- F-distribution
- Edgeworth expansion
- Monotone likelihood ratio
- Sample mean and sample covariance
- Metropolis–Hastings algorithm
- Continuous-time Markov chain
- Minimal sufficient statistic
- Whittle likelihood
- Logistic regression
- Convergence to equilibrium of ergodic Markov chains
- Azuma's inequality
- Gaussian free field
- Kernel density estimation
- Erdős–Kac theorem
- Detailed balance
- Coupling from the past
- Hidden Markov model
- Continuous mapping theorem
- 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
- Kruskal–Wallis test
- Information geometry
- Recurrence and transience of states
- Malliavin calculus
- Wishart distribution
- Birth–death process
- Minimum-variance unbiased estimator
- Slutsky's theorem
- Tanaka's formula
- Empirical likelihood
- Bayesian information criterion
- Berry–Esseen theorem
- Cochran's theorem
- Stochastic approximation
- Jackknife
- Markov chain mixing time
- Birkhoff's ergodic theorem
- Akaike information criterion
- Credible interval
- Independence of sample mean and sample variance for normal samples
- Gibbs sampling
- Estimating equations
- Supermartingale
- Spearman's rank correlation coefficient
- Markov chain Monte Carlo
- Generalized method of moments
- Doob's martingale convergence theorems
- Bernstein inequalities
- Semimartingale
- Bootstrap
- Influence function
- Oseledets theorem
- Itô calculus
- Moments of the log-normal distribution
- Complete statistic
- Horvitz–Thompson estimator
- U-statistic
- Sufficient statistic
- Missing data mechanisms
- Likelihood-ratio test
- Conjugate prior
- Expectation–maximization algorithm
- Hotelling's T-squared distribution
- Burkholder–Davis–Gundy inequality
- Doob–Meyer decomposition theorem
- Wald test
- Generalized linear model
- Box–Jenkins method
- Pesin theory
- 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
- Observed information
- Point estimation
- Local martingale
Further reading
- Web https://en.wikipedia.org/wiki/Probability_density_function, Absolutely continuous univariate distributions
About this page
| Tag | 0C21RZG5V2 |
|---|---|
| URI | m2://0C21RZG5V2 |
| Kind | definition |
| Writing | W0 stub |
| MSC | 60E05 |
| Defines | Random variable with a density |
| Uses | Random variable, Borel sigma-algebra |
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