6.3.9.6.3. statsmodels.sandbox.distributions.mv_normal.MVNormal¶
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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
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__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
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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 |