Multivariate Verteilungen
Eine multivariate Verteilung ist eine Wahrscheinlichkeitsverteilung, die mehr als eine zufällige Variable enthält. Die zufälligen Variablen könnten korreliert sein. Statistics and Machine Learning Toolbox™ umfasst mehrere Methoden zur Arbeit mit multivariaten Verteilungen, darunter Wahrscheinlichkeitsverteilungsobjekte, Verteilungsfunktionen und interaktive Apps. Weitere Informationen finden Sie unter Working with Probability Distributions.
Objekte
gmdistribution | Create Gaussian mixture model |
MultinomialDistribution | Multinomial probability distribution object |
Funktionen
Themen
- 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.
