Probability theory and stochastic processes Definition
Probability density function
Builds on Random variable with a densityCumulative distribution function
Definition
Let be a real random variable on a probability space . A probability density function of is a Borel measurable function such that for each Borel set . So has a probability density function if and only if is a random variable with a density.
Remarks
Each density satisfies . Two densities of are equal except on a set of Lebesgue measure zero. for . If the cumulative distribution function is differentiable at and is continuous at , then . A density can be larger than . The uniform distribution on has the density on this interval.
Reading path
To understand this page from the ground up, read these 42 pages in order. The foundations come first.
- 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 distribution definition
- Probability space definition
- Random variable definition
- Cumulative distribution function definition
- Random variable with a density definition
- Probability density function definition
Where this leads
110 pages build on this one.
- Chernoff bound
- Law of the unconscious statistician
- Density of the normal distribution
- Exponential distribution
- Characteristic function of a sum of independent random variables
- Variance of the normal distribution
- Likelihood function
- Poisson point process
- Gamma distribution
- De Moivre–Laplace theorem
- Convolution of probability distributions
- Normal distribution
- Method of moments
- Mean of the normal distribution
- Moment-generating function of the normal distribution
- Uncorrelated random variables that are not independent
- Kurtosis
- Neyman–Pearson lemma
- P-value
- Chi-squared test
- Central limit theorem
- 68–95–99.7 rule
- Lévy's continuity theorem
- Maximum likelihood estimation
- Multivariate normal distribution
- Skewness
- Student's t-test
- Bayesian inference
- Confidence interval
- Characteristic function
- Cauchy distribution
- Moment-generating function
- Student's t-distribution
- Characteristic function of the normal distribution
- Sum of independent normal random variables
- Moment
- Beta distribution
- Cumulant generating function
- Chi-squared distribution
- Moments are derivatives of the moment-generating function at zero
- The characteristic function determines the distribution
- Continuous uniform distribution
- Hoeffding's inequality
- Moment-generating function of a sum of independent random variables
- Conditional probability distribution
- Memorylessness
- Central moment
- Bernstein–von Mises theorem
- Poisson regression
- Koksma–Hlawka inequality
- Hodges' superefficient estimator
- Cramér–Wold theorem
- Boolean model
- Wald–Wolfowitz optimality of the sequential probability ratio test
- Consistency of the maximum likelihood estimator
- Bayes factor
- Contiguity of measures
- Lindley's paradox
- M-estimator
- Score test
- McDiarmid's inequality
- Asymptotic normality of the maximum likelihood estimator
- Score
- F-distribution
- Edgeworth expansion
- Monotone likelihood ratio
- Whittle likelihood
- Logistic regression
- Azuma's inequality
- Kernel density estimation
- Erdős–Kac theorem
- Monte Carlo integration
- Delta method
- Noncentral chi-squared distribution
- Hájek–Le Cam convolution theorem
- Behrens–Fisher problem
- Kruskal–Wallis test
- Information geometry
- Wishart distribution
- Empirical likelihood
- Bayesian information criterion
- Berry–Esseen theorem
- Cochran's theorem
- Akaike information criterion
- Credible interval
- Independence of sample mean and sample variance for normal samples
- Estimating equations
- Generalized method of moments
- Bernstein inequalities
- Moments of the log-normal distribution
- Missing data mechanisms
- Likelihood-ratio test
- Conjugate prior
- Expectation–maximization algorithm
- Hotelling's T-squared distribution
- Wald test
- Generalized linear model
- Box–Jenkins method
- Binomial proportion confidence interval
- False discovery rate and the Benjamini–Hochberg procedure
- Neyman construction
- Wilks' theorem
- Permutation test
- Fisher's exact test
- Bonferroni correction
- Local asymptotic normality
- Frequentist inference
- Likelihood principle
- Observed information
- Point estimation
Further reading
- Web https://en.wikipedia.org/wiki/Probability_density_function, Formal definition
About this page
| Tag | 04YG690N5V |
|---|---|
| URI | m2://04YG690N5V |
| Kind | definition |
| Writing | W1 stated |
| MSC | 60E05 |
| Defines | Probability density function |
| Uses | Random variable with a density, Cumulative distribution function |
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What links here
Used by
- Law of the unconscious statistician
- Density of the normal distribution
- Exponential distribution
- Likelihood function
- Gamma distribution
- Convolution of probability distributions
- Normal distribution
- Cauchy distribution
- Beta distribution
- Chi-squared distribution
- Continuous uniform distribution
- Conditional probability distribution
- F-distribution
- Monotone likelihood ratio
- Kernel density estimation
- Information geometry
- Moments of the log-normal distribution