Journal article
Efficient computation of confidence intervals for Bayesian model predictions based on multidimensional parameter space
Methods in enzymology, Vol.454, pp.213-231
2009
Handle:
https://hdl.handle.net/2376/115973
PMID: 19216928
Abstract
A new algorithm is introduced to efficiently estimate confidence intervals for Bayesian model predictions based on multidimensional parameter space. The algorithm locates the boundary of the smallest confidence region in the multidimensional probability density function (pdf) for the model predictions by approximating a one-dimensional slice through the mode of the pdf with splines made of pieces of normal curve with continuous z values. This computationally efficient process (of order N) reduces estimation of the lower and upper bounds of the confidence interval to a multidimensional constrained nonlinear optimization problem, which can be solved with standard numerical procedures (of order N(2) or less). Application of the new algorithm is illustrated with a five-dimensional example involving the computation of 95% confidence intervals for predictions made with a Bayesian forecasting model for cognitive performance deficits of sleep-deprived individuals.
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Details
- Title
- Efficient computation of confidence intervals for Bayesian model predictions based on multidimensional parameter space
- Creators
- Amber D Smith - Sleep and Performance Research Center, Washington State University, Spokane, Washington, USAAlan GenzDavid M FreibergerGregory BelenkyHans P A Van Dongen
- Publication Details
- Methods in enzymology, Vol.454, pp.213-231
- Academic Unit
- Mathematics and Statistics, Department of; Medical Education and Clinical Science, Department of
- Publisher
- United States
- Identifiers
- 99900548586001842
- Language
- English
- Resource Type
- Journal article