6.3.9.6.3. statsmodels.sandbox.distributions.mv_normal.MVNormal

class statsmodels.sandbox.distributions.mv_normal.MVNormal(mean, sigma, *args, **kwds)[source]

Class for Multivariate Normal Distribution

uses Cholesky decomposition of covariance matrix for the transformation of the data

initialize instance

Parameters:

mean : array_like

parameter mu (might be renamed), for symmetric distributions this is the mean

sigma : array_like, 2d

dispersion matrix, covariance matrix in normal distribution, but only proportional to covariance matrix in t distribution

args : list

distribution specific arguments, e.g. df for t distribution

kwds : dict

currently not used

__init__(mean, sigma, *args, **kwds)

initialize instance

Parameters:

mean : array_like

parameter mu (might be renamed), for symmetric distributions this is the mean

sigma : array_like, 2d

dispersion matrix, covariance matrix in normal distribution, but only proportional to covariance matrix in t distribution

args : list

distribution specific arguments, e.g. df for t distribution

kwds : dict

currently not used

6.3.9.6.3.1. Methods

__init__(mean, sigma, *args, **kwds) initialize instance
affine_transformed(shift, scale_matrix) return distribution of an affine transform
cdf(x, **kwds) cumulative distribution function
conditional(indices, values) return conditional distribution
expect_mc(dist[, func, size]) calculate expected value of function by Monte Carlo integration
logpdf(x) logarithm of probability density function
marginal(indices) return marginal distribution for variables given by indices
normalize(x) normalize the random variable, i.e. subtract mean and rescale
normalized([demeaned]) return a normalized distribution where sigma=corr
normalized2([demeaned]) return a normalized distribution where sigma=corr
pdf(x) probability density function
rvs([size]) random variable
standardize(x) standardize the random variable, i.e. subtract mean and whiten
standardized() return new standardized MVNormal instance
whiten(x) whiten the data by linear transformation

6.3.9.6.3.2. Attributes

corr correlation matrix
cov covariance matrix
std standard deviation, square root of diagonal elements of cov
std_sigma standard deviation, square root of diagonal elements of sigma