https://github.com/olivierverdier/pdmatssingular.jl

An extension of PDMats to use singular covariance matrices
https://github.com/olivierverdier/pdmatssingular.jl

Last synced: about 5 hours ago
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An extension of PDMats to use singular covariance matrices

• Host: GitHub
• URL: https://github.com/olivierverdier/pdmatssingular.jl
• Owner: olivierverdier
• License: mit
• Created: 2024-07-15T19:54:54.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-08-29T07:10:59.000Z (3 months ago)
• Last Synced: 2024-08-29T08:38:49.272Z (3 months ago)
• Language: Julia
• Homepage: https://olivierverdier.github.io/PDMatsSingular.jl/
• Size: 221 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# PDMatsSingular

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[![](https://img.shields.io/badge/docs-dev-blue.svg)](https://olivierverdier.github.io/PDMatsSingular.jl/)

This package allows to use `PDMats` with singular covariance matrices.

For instance:

```julia

Σ = covariance_from([1; 0;;])

```

Then:

```julia

Matrix(Σ)

```

gives

```

2×2 Matrix{Int64}:

 1  0

 0  0

```

The random variable with this covariance can be sampled as follows.

With

```julia

rng = Random.default_rng()

```

```julia

sample(rng, Σ)

```

gives samples with the singular covariance above, that is, the second component is guaranteed to be zero.