Identifiability of parametric random matrix models

Abstract

We investigate parameter identifiability of spectral distributions of random matrices. In particular, we treat compound Wishart type and signal-plus-noise type. We show that each model is identifiable up to some kind of rotation of parameter space. Our method is based on free probability theory.

References in corpus (3)

• Identifiability of parameters in latent structure models with many observed variables
• Multiplicative free Convolution and Information-Plus-Noise Type Matrices
• Cauchy noise loss for stochastic optimization of random matrix models via free deterministic equivalents