Predicting conditional probability distributions of redshifts of Active Galactic Nuclei using Hierarchical Correlation Reconstruction

TL;DR

This paper introduces a method using Hierarchical Correlation Reconstruction combined with CCA and l1 regularization to predict complex, multimodal conditional probability distributions of AGN redshifts, improving uncertainty estimation.

Contribution

It extends HCR with CCA and lasso regularization to effectively predict complex distributions of AGN redshifts from Fermi-LAT data, emphasizing interpretability and practical application.

Findings

Successfully predicts multimodal redshift distributions.

Provides interpretable models with feature contributions.

Enhances uncertainty estimation in redshift prediction.

Abstract

While there is a general focus on prediction of values, real data often only allows to predict conditional probability distributions, with capabilities bounded by conditional entropy $H(Y|X)$. If additionally estimating uncertainty, we can treat a predicted value as the center of Gaussian of Laplace distribution - idealization which can be far from complex conditional distributions of real data. This article applies Hierarchical Correlation Reconstruction (HCR) approach to inexpensively predict quite complex conditional probability distributions (e.g. multimodal): by independent MSE estimation of multiple moment-like parameters, which allow to reconstruct the conditional distribution. Using linear regression for this purpose, we get interpretable models: with coefficients describing contributions of features to conditional moments. This article extends on the original approach especially by using Canonical Correlation Analysis (CCA) for feature optimization and l1 "lasso" regularization, focusing on practical problem of prediction of redshift of Active Galactic Nuclei (AGN) based on Fourth Fermi-LAT Data Release 2 (4LAC) dataset.