Package for Multivariate (normal) Conditional Distribution

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Hi! Is any package to get Multivariate (normal) Conditional Distribution?

Something like described here.

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maybe other people jump in that know more, but this seems related:

in particular, also the last answer therein and reference to the conditional(P, A, B, xB) method from GaussianDistributions.jl which should do what you want.

essentially you could ā€œjustā€ calculate \mu_{1|2} and \Sigma_{1|2} yourself as described in your link and then sample from MvNormal(\mu_{1|2}, \Sigma_{1|2}) via Distributions.jl. Advantage of the above references may be a good numerical implementation for computing \mu_{1|2} and \Sigma_{1|2}.

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[Shameless propaganda] Just to inform you if you still care that now Copulas.jl does allow conditioning or multivariate random vectors. It has a generic AD-based Implementation, but for Gaussianā€™s and Studentā€™s dependence structures, it does the smart parametric thing:

using Copulas, Distributions
rho = 0.7 # could be a full correlation matrix as well.
mu = [0, 1, -2]
sigma = [1, 1, 2]
S = SklarDist(GaussianCopula(3, rho), Normal.(mu, sigma)) # this is now a trivariate gaussian random vector.

D1 = condition(S, 2, 0.3)  # gives a bivariate random vector. 
D2 = condition(S, (1,2), (0.3, 0.4)) # gives a Normal() with correct mean and variance.

see the docs there : Conditioning and Subsetting | Copulas.jl

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