An invariant bayesian model selection principle for gaussian data in a sparse representation

TL;DR

This paper introduces an invariant Bayesian model selection principle for Gaussian data, providing new estimators and bounds, and demonstrating its effectiveness in wavelet-based Gaussian estimation tasks.

Contribution

It develops an invariant code length principle with practical approximation formulas and unbiased estimators, enhancing model selection for Gaussian models in sparse representations.

Findings

The proposed method achieves near-optimal code length matching Rissanen's NML.

Invariant estimators are unbiased under certain conditions.

Numerical experiments show improved performance over existing wavelet-based methods.

Abstract

We develop a code length principle which is invariant to the choice of parameterization on the model distributions. An invariant approximation formula for easy computation of the marginal distribution is provided for gaussian likelihood models. We provide invariant estimators of the model parameters and formulate conditions under which these estimators are essentially posteriori unbiased for gaussian models. An upper bound on the coarseness of discretization on the model parameters is deduced. We introduce a discrimination measure between probability distributions and use it to construct probability distributions on model classes. The total code length is shown to equal the NML code length of Rissanen to within an additive constant when choosing Jeffreys prior distribution on the model parameters together with a particular choice of prior distribution on the model classes. Our model selection principle is applied to a gaussian estimation problem for data in a wavelet representation and its performance is tested and compared to alternative wavelet-based estimation methods in numerical experiments