A variational EM approach to predictive uncertainty

Harva, Markus

doi:10.1016/j.neunet.2007.04.010

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…Reviews on Bayesian neural networks can be found in Lampinen and Vehtari (2001) and Titterington (2004). Some variants of Bayesian neural networks include those by Rohekar et al (2019) who assigned prior distributions that depend on unlabelled predictors and Harva (2007), where the distribution of the dependent variable is a mixture of Gaussian distributions. The latter approach allows modelling the dependent variable with non-Gaussian distributions, thus being more flexible.…”

Section: Bayesian Methodsmentioning

confidence: 99%

A review of predictive uncertainty estimation with machine learning

Tyralis,

Papacharalampous

2024

Artif Intell Rev

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Predictions and forecasts of machine learning models should take the form of probability distributions, aiming to increase the quantity of information communicated to end users. Although applications of probabilistic prediction and forecasting with machine learning models in academia and industry are becoming more frequent, related concepts and methods have not been formalized and structured under a holistic view of the entire field. Here, we review the topic of predictive uncertainty estimation with machine learning algorithms, as well as the related metrics (consistent scoring functions and proper scoring rules) for assessing probabilistic predictions. The review covers a time period spanning from the introduction of early statistical (linear regression and time series models, based on Bayesian statistics or quantile regression) to recent machine learning algorithms (including generalized additive models for location, scale and shape, random forests, boosting and deep learning algorithms) that are more flexible by nature. The review of the progress in the field, expedites our understanding on how to develop new algorithms tailored to users’ needs, since the latest advancements are based on some fundamental concepts applied to more complex algorithms. We conclude by classifying the material and discussing challenges that are becoming a hot topic of research.

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Section: Bayesian Methodsmentioning

confidence: 99%