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SymmetricMatrixvariateNormal as a separate RandomVariable #431

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marvinpfoertner opened this issue Jun 15, 2021 · 2 comments
Open
1 task

SymmetricMatrixvariateNormal as a separate RandomVariable #431

marvinpfoertner opened this issue Jun 15, 2021 · 2 comments
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randvars Issues related to random variables refactoring Refactoring of existing functionality

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@marvinpfoertner
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marvinpfoertner commented Jun 15, 2021

Current State

All variants of a Gaussian random variable share a joint interface.

Problem

However, a Gaussian measure on symmetric matrices behaves fundamentally different from a matrixvariate normal distribution with a SymmetricKronecker covariance. This is the case since support is not the same (only symmetric matrices are sampled from a symmetric matrix-variate distribution).

def _symmetric_kronecker_identical_factors_cov_cholesky(

Proposed Solution

  • Implement symmetric matrix-variate normal random variables in a different subclass of RandomVariable.
@marvinpfoertner marvinpfoertner added randvars Issues related to random variables refactoring Refactoring of existing functionality labels Jun 15, 2021
@marvinpfoertner marvinpfoertner added this to the Initial Release milestone Jun 15, 2021
@mmahsereci
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I would second this. One question: Does this only concern SymmetricMatrixVariateNormal, or do we also support the non-symmetric version? (just curious, not saying we need to support the non-symmetric case as well atm)

@JonathanWenger
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I would second this. One question: Does this only concern SymmetricMatrixVariateNormal, or do we also support the non-symmetric version? (just curious, not saying we need to support the non-symmetric case as well atm)

We currently support non-symmetric matrix-variate normal distributions by passing an appropriate mean (as a matrix or linear operator).

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Labels
randvars Issues related to random variables refactoring Refactoring of existing functionality
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