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Quantile Forecast Matching with a Bayesian Quantile Gaussian Process Model by Wadsworth, Spencer; Niemi, Jarad is a scholarly article available to read on EtoBox.

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A set of probabilities along with corresponding quantiles are often used to define predictive distributions or probabilistic forecasts. These quantile predictions offer easily interpreted uncertainty of an event, and quantiles are generally straightforward to estimate using standard statistical and machine learning methods. However, compared to a distribution defined by a probability density or cumulative distribution function, a set of quantiles has less distributional information. When given estimated quantiles, it may be desirable to estimate a fully defined continuous distribution function. Many researchers do so to make evaluation or ensemble modeling simpler. Most existing methods for fitting a distribution to quantiles lack accurate representation of the inherent uncertainty from quantile estimation or are limited in their applications. In this manuscript, we present a Gaussian process model, the quantile Gaussian process, which is based on established theory of quantile functions and sample quantiles, to construct a probability distribution given estimated quantiles. A Bayesian application of the quantile Gaussian process is evaluated for parameter inference and distributio

Author

Wadsworth, Spencer; Niemi, Jarad

Published

2025

Language

EN