multivariate density
1Multivariate Student distribution — Multivariate Student parameters: location (real vector) Σ scale matrix (positive definite real matrix) n is the degree of freedom support …
2Multivariate kernel density estimation — Kernel density estimation is a nonparametric technique for density estimation i.e., estimation of probability density functions, which is one of the fundamental questions in statistics. It can be viewed as a generalisation of histogram density… …
3Density estimation — In probability and statistics, density estimation is the construction of an estimate, based on observed data, of an unobservable underlying probability density function. The unobservable density function is thought of as the density according to… …
4Multivariate statistics — is a form of statistics encompassing the simultaneous observation and analysis of more than one statistical variable. The application of multivariate statistics is multivariate analysis. Methods of bivariate statistics, for example simple linear… …
5Multivariate analysis of variance — (MANOVA) is a generalized form of univariate analysis of variance (ANOVA). It is used when there are two or more dependent variables. It helps to answer : 1. do changes in the independent variable(s) have significant effects on the dependent …
6Multivariate stable distribution — multivariate stable Probability density function Heatmap showing a Multivariate (bivariate) stable distribution with α = 1.1 parameters: exponent shift/location vector …
7Multivariate normal distribution — MVN redirects here. For the airport with that IATA code, see Mount Vernon Airport. Probability density function Many samples from a multivariate (bivariate) Gaussian distribution centered at (1,3) with a standard deviation of 3 in roughly the… …
8Multivariate gamma function — In mathematics, the multivariate Gamma function, Γp(·), is a generalization of the Gamma function. It is useful in multivariate statistics, appearing in the probability density function of the Wishart and Inverse Wishart distributions. It has two …
9Kernel density estimation — of 100 normally distributed random numbers using different smoothing bandwidths. In statistics, kernel density estimation is a non parametric way of estimating the probability density function of a random variable. Kernel density estimation is a… …
10Bayesian multivariate linear regression — Consider a collection of m linear regression problems for n observations, related through a set of common predictor variables {x {c}}, and a jointly normal errors {epsilon {c}} ::y {1} = eta {1} x {1} + epsilon {1},,:y {c} = eta {c} x {c} +… …
