La traduction de cette page n'est pas à jour. Cliquez ici pour voir la dernière version en anglais.
Distributions multivariées
R2026aUne distribution multivariée est une loi de probabilité contenant plusieurs variables aléatoires. Ces dernières peuvent être corrélées. Statistics and Machine Learning Toolbox™ propose plusieurs façons de travailler avec des distributions multivariées, notamment des objets de distribution de probabilité, des fonctions de distribution et des applications interactives. Pour plus d’informations, consultez Working with Probability Distributions.
Objets
gmdistribution | Create Gaussian mixture model |
MultinomialDistribution | Multinomial probability distribution object |
Fonctions
Rubriques
- Generate Correlated Samples Using Copulas
Copulas are functions that describe dependencies among variables, and provide a way to create distributions that model correlated multivariate data.
- Generate Correlated Data Using Rank Correlation
Use a copula and rank correlation to generate correlated data from probability distributions that do not have an inverse cdf function available, such as the Pearson flexible distribution family.
- Simulate Dependent Random Variables Using Copulas
Use copulas to generate data from multivariate distributions with complicated relationships among the variables, or with the individual variables from different distributions.
- Create Gaussian Mixture Model
Create a known, or fully specified, Gaussian mixture model (GMM) object.
- Fit Gaussian Mixture Model to Data
Simulate data from a multivariate normal distribution, and then fit a Gaussian mixture model (GMM) to the data.
- Simulate Data from Gaussian Mixture Model
Simulate data from a Gaussian mixture model (GMM) using a fully specified
gmdistributionobject and therandomfunction. - Cluster Data Using Gaussian Mixture Model
Partition data into clusters with different sizes and correlation structures.
- Inverse Wishart Distribution
The inverse Wishart distribution, which is based on the Wishart distribution, is used in Bayesian statistics as the conjugate prior for the covariance matrix of a multivariate normal distribution.
- Multinomial Distribution
The multinomial distribution models the probability of each combination of successes in a series of independent trials.
- Multivariate Normal Distribution
The multivariate normal distribution is a generalization of the univariate normal to two or more variables.
- Multivariate t Distribution
The multivariate Student's t distribution is a generalization of the univariate Student's t to two or more variables.
- Wishart Distribution
The Wishart distribution is a generalization of the univariate chi-square distribution to two or more variables.
- Maximum Likelihood Estimation
The
mlefunction computes maximum likelihood estimates (MLEs) for a distribution specified by its name and for a custom distribution specified by its probability density function (pdf), log pdf, or negative log likelihood function. - Negative Loglikelihood Functions
Find maximum likelihood estimates using negative loglikelihood functions.
