# Multivariate Gaussian and Student$-t$ Process Regression for Multi-output Prediction

## TL;DR

This paper introduces a unified framework for multivariate Gaussian and Student-t process regression models, enabling effective multi-output prediction with closed-form solutions, and demonstrates their advantages through various real-world applications.

## Contribution

A novel unified framework that models both multivariate Gaussian and Student-t processes for multi-output prediction, overcoming limitations of existing reformulation methods.

## Key findings

- MV-TPR outperforms existing models in air quality and bike rent prediction.
- Both models have closed-form marginal likelihoods and predictive distributions.
- Proposed methods enable profitable stock market investment strategies.

## Abstract

Gaussian process model for vector-valued function has been shown to be useful for multi-output prediction. The existing method for this model is to re-formulate the matrix-variate Gaussian distribution as a multivariate normal distribution. Although it is effective in many cases, re-formulation is not always workable and is difficult to apply to other distributions because not all matrix-variate distributions can be transformed to respective multivariate distributions, such as the case for matrix-variate Student$-t$ distribution. In this paper, we propose a unified framework which is used not only to introduce a novel multivariate Student$-t$ process regression model (MV-TPR) for multi-output prediction, but also to reformulate the multivariate Gaussian process regression (MV-GPR) that overcomes some limitations of the existing methods. Both MV-GPR and MV-TPR have closed-form expressions for the marginal likelihoods and predictive distributions under this unified framework and thus can adopt the same optimization approaches as used in the conventional GPR. The usefulness of the proposed methods is illustrated through several simulated and real data examples. In particular, we verify empirically that MV-TPR has superiority for the datasets considered, including air quality prediction and bike rent prediction. At last, the proposed methods are shown to produce profitable investment strategies in the stock markets.