Results 11 to 20 of about 1,703,012 (264)
On recursive Bayesian predictive distributions [PDF]
Journal of the American Statistical Association, 2017 A Bayesian framework is attractive in the context of prediction, but a fast recursive update of the predictive distribution has apparently been out of reach, in part because Monte Carlo methods are generally used to compute the predictive.
Hahn, P. Richard +2 more
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Distributional conformal prediction [PDF]
Proceedings of the National Academy of Sciences, 2021 Significance Prediction problems are important in many contexts. Examples include cross-sectional prediction, time series forecasting, counterfactual prediction and synthetic controls, and individual treatment effect prediction. We develop a prediction method that works in conjunction with many powerful classical methods (e.g., conventional ...
Victor Chernozhukov +2 more
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Distribution-Free Prediction Sets [PDF]
Journal of the American Statistical Association, 2013 This paper introduces a new approach to prediction by bringing together two different nonparametric ideas: distribution free inference and nonparametric smoothing. Specifically, we consider the problem of constructing nonparametric tolerance/prediction sets.
Jing, Lei +2 more
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Predicting Disparity Distributions
2021 IEEE International Conference on Robotics and Automation (ICRA), 2021 We investigate a novel deep-learning-based approach to estimate uncertainty in stereo disparity prediction networks. Current state-of-the-art methods often formulate disparity prediction as a regression problem with a single scalar output in each pixel.
Häger, Gustav +2 more
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Combining predictive distributions
Electronic Journal of Statistics, 2013 Predictive distributions need to be aggregated when probabilistic forecasts are merged, or when expert opinions expressed in terms of probability distributions are fused. We take a prediction space approach that applies to discrete, mixed discrete-continuous and continuous predictive distributions alike, and study combination formulas for cumulative ...
Tilmann Gneiting, Roopesh Ranjan
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Marginal and simultaneous predictive classification using stratified graphical models [PDF]
, 2014 An inductive probabilistic classification rule must generally obey the principles of Bayesian predictive inference, such that all observed and unobserved stochastic quantities are jointly modeled and the parameter uncertainty is fully acknowledged ...
Corander, Jukka +3 more
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Predictable Return Distributions [PDF]
SSRN Electronic Journal, 2010 Using quantile regression this paper explores the predictability of the stock and bond return distributions as a function of economic state variables. The use of quantile regression allows us to examine specific parts of the return distribution such as the tails and the center, and for a sufficiently fine grid of quantiles we can trace out the entire ...
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Choosing priors in Bayesian ecological models by simulating from the prior predictive distribution
Ecosphere, 2021 Bayesian data analysis is increasingly used in ecology, but prior specification remains focused on choosing non‐informative priors (e.g., flat or vague priors).
Jeff S. Wesner, Justin P. F. Pomeranz
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Mathematics, 2023 The quality characteristic(s) are assumed to follow the normal distribution in many control chart constructions, although this assumption may not hold in some instances.
Fatimah Alshahrani +5 more
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iForest - Biogeosciences and Forestry, 2022 The different models that predict the distribution of species are a useful tool for the evaluation and monitoring of forest resources as they facilitate the planning of their management in a changing climate environment. Recently, a significant number of
Montoya-Jiménez JC +4 more
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