# Parametric Gaussian Process Regression for Big Data

## TL;DR

This paper proposes parametric Gaussian processes (PGPs) for big data, offering a scalable alternative to stochastic variational inference, and demonstrates their effectiveness on large-scale datasets.

## Contribution

It introduces the novel concept of parametric Gaussian processes that operate efficiently in big data settings without relying on stochastic variational inference.

## Key findings

- Effective on simulated data
- Performs well on airline industry benchmark dataset
- Avoids the need for stochastic variational inference

## Abstract

This work introduces the concept of parametric Gaussian processes (PGPs), which is built upon the seemingly self-contradictory idea of making Gaussian processes parametric. Parametric Gaussian processes, by construction, are designed to operate in "big data" regimes where one is interested in quantifying the uncertainty associated with noisy data. The proposed methodology circumvents the well-established need for stochastic variational inference, a scalable algorithm for approximating posterior distributions. The effectiveness of the proposed approach is demonstrated using an illustrative example with simulated data and a benchmark dataset in the airline industry with approximately 6 million records.