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

arXiv: https://arxiv.org/abs/1703.04455 · HTML: https://mlsanity.com/paper/1703.04455/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2017.

## Built on by (most cited first, 14 of 14)

- [Sparse Variational Student-t Processes](https://mlsanity.com/paper/2312.05568/) (2023)
- [Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalization](https://mlsanity.com/paper/2109.09261/) (2021)
- [B'ezier Curve Gaussian Processes](https://mlsanity.com/paper/2205.01754/) (2022)
- [Contrastive inverse regression for dimension reduction](https://mlsanity.com/paper/2305.12287/) (2023)
- [Sparse Variational Student-t Processes for Heavy-tailed Modeling](https://mlsanity.com/paper/2408.06699/) (2024)
- [Multi-Output Robust and Conjugate Gaussian Processes](https://mlsanity.com/paper/2510.26401/) (2025)
- [Information Flow Rate for Cross-Correlated Stochastic Processes](https://mlsanity.com/paper/2401.04950/) (2024)
- [When Bayesian Tensor Completion Meets Multioutput Gaussian Processes: Functional Universality and Rank Learning](https://mlsanity.com/paper/2512.21486/) (2025)
- [Predictive Accuracy versus Interpretability in Energy Markets: A Copula-Enhanced TVP-SVAR Analysis](https://mlsanity.com/paper/2601.19321/) (2026)
- [Variational Bayes Portfolio Construction](https://mlsanity.com/paper/2411.06192/) (2024)
- [Convolution-Based Converter : A Weak-Prior Approach For Modeling Stochastic Processes Based On Conditional Density Estimation](https://mlsanity.com/paper/2502.06829/) (2025)
- [Transfer Learning Across Fixed-Income Product Classes](https://mlsanity.com/paper/2505.07676/) (2025)
- [ViSTR-GP: Online Cyberattack Detection via Vision-to-State Tensor Regression and Gaussian Processes in Automated Robotic Operations](https://mlsanity.com/paper/2509.10948/) (2025)
- [Combinatorial Bandit Bayesian Optimization for Tensor Outputs](https://mlsanity.com/paper/2602.00640/) (2026)

## Abstract (arXiv)

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.