Results 291 to 300 of about 172,572 (332)

Credible Interval Temperature Forecasts

Bulletin of the American Meteorological Society, 1972
Abstract An experiment was conducted in which forecasters expressed temperature forecasts in terms of intervals of variable width and fixed probability. The use of such intervals, called credible intervals, permits forecasters to describe the uncertainty inherent in their temperature forecasts in a meaningful, quantitative way.
Cameron R. Peterson, Kurt J. Snapper, Allan H. Murphy   +2 more
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Approximate Credibility Intervals for Independent Component Analysis

2021
Independent component analysis (ICA) is often used to retrieve the activation patterns of motor units (MUs) from electromyographic (EMG) data. This chapter uses a Bayesian approach to look at the uncertainties around the ICA results. This is done both in regards to the amount of training data provided and in regard to how the certainty differs for each
Olivier Thill, Luca Citi
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Coverage of credible intervals in nonparametric monotone regression

The Annals of Statistics, 2021
This paper concerns a nonparametric regression model for a response variable \(Y\) with respect to a predictor variable \(X\in [0, 1]\) given by \(Y = f (X)+\varepsilon\), where \(f\) is a monotone increasing function on \([0, 1]\) and \(\varepsilon\) is a mean-zero random error with finite variance \(\sigma^2\).
Chakraborty, Moumita, Ghosal, Subhashis
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Credible Interval Computation in Weather Derivatives Pricing

SSRN Electronic Journal, 2012
This paper examines the quantification of uncertainty in weather derivatives pricing. The focus is on the propagation of a posterior distribution on uncertain model parameters through to relevant payoff statistics, summarizing the uncertainty using variance based credible intervals for any given payoff statistic.
Shree Khare, Stephen Jewson
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A Modification for Bayesian Credible Intervals

Communications in Statistics - Theory and Methods, 2006
In Bayesian analysis, people usually report the highest posterior density (HPD) credible interval as an interval estimate of an unknown parameter. However, when the unknown parameter is the nonnegative normal mean, the Bayesian HPD credible interval under the uniform prior has quite a low minimum frequentist coverage probability. To enhance the minimum
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Computing Bayesian Credible and HPD Intervals

2000
One purpose of Bayesian posterior inference is to summarize posterior marginal densities. Graphical presentation of the entire posterior distribution is always desirable if this can be conveniently accomplished. However, summary statistics, which outline important features of the posterior distribution, are sometimes adequate.
Ming-Hui Chen, Qi-Man Shao, Joseph G. Ibrahim   +2 more
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Bayesian credible intervals for response surface optima

Journal of Statistical Planning and Inference, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fox, Richard J., Elgart, David, Davis, S. Christopher   +2 more
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Robust Bayesian Credible Intervals and Prior Ignorance

International Statistical Review / Revue Internationale de Statistique, 1991
Summary In this paper we propose, survey and compare some classes of probability densities that may be used to represent partial prior information, to model either prior ignorance or Bayesian sensitivity analysis. We distinguish two types of models appropriate for two different situations: near ignorance models which are suitable in problems where ...
Luis Raúl Pericchi, Peter Walley, Luis Raul Pericchi   +2 more
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Confusion of confidence intervals and credibility intervals in meta-analysis.

Journal of Applied Psychology, 1990
A review of 30 meta-analyses that have been conducted in organizational behavior and human resource management using procedures described by Hunter, Schmidt, and Jackson (1982) suggests that there is confusion regarding the use and interpretation of confidence intervals and credibility intervals. This confusion can lead to conflicting conclusions about
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Credible intervals and rankogram distributions

Anaesthesia, 2022
P. M. Singh, A. Borle, D. Monks
openaire   +2 more sources