Joint estimation of the predictive ability of experts using a multi-output Gaussian process

2024/02/12 by Oelrich, Oscar, Villani, Mattias
#FOS: Computer and information sciences #Methodology (stat.ME)

paper · doi:10.48550/arxiv.2402.07439

Abstract

A multi-output Gaussian process (GP) is introduced as a model for the joint posterior distribution of the local predictive ability of set of models and/or experts, conditional on a vector of covariates, from historical predictions in the form of log predictive scores. Following a power transformation of the log scores, a GP with Gaussian noise can be used, which allows faster computation by first using Hamiltonian Monte Carlo to sample the hyper-parameters of the GP from a model where the latent GP surface has been marginalized out, and then using these draws to generate draws of joint predictive ability conditional on a new vector of covariates. Linear pools based on learned joint local predictive ability are applied to predict daily bike usage in Washington DC.

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• Modeling local predictive ability using power-transformed Gaussian processes
• Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions
• Local Prediction Pools
• Healing Products of Gaussian Processes
• Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression