Bayesian forecasting in economics and finance: A modern review

Martin, Gael M.; Frazier, David T.; Maneesoonthorn, Worapree; Loaiza-Maya, Rubén; Huber, Franz; Koop, Gary; Maheu, John M.; Nibbering, Didier; Panagiotelis, Anastasios

doi:10.1016/j.ijforecast.2023.05.002

Cited by 5 publications

(1 citation statement)

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“…This approach enables the evaluation of the information conveyed via diagnostic measurements and the combination of data from multiple diagnostic tests, which may improve diagnostic accuracy and precision while introducing flexibility, adaptability, and versatility Diagnostics 2023, 13, 3135 2 of 28 into the diagnostic process [10]. Furthermore, the Bayesian approach extends its utility beyond the medical field by offering a robust framework for quantifying uncertainty in various domains, thereby enriching its applicability in both diagnostic and prognostic contexts [11,12].…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Inference Based Computational Tool for Parametric and Nonparametric Medical Diagnosis

Chatzimichail,

Hatjimihail

2023

Diagnostics

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Medical diagnosis is the basis for treatment and management decisions in healthcare. Conventional methods for medical diagnosis commonly use established clinical criteria and fixed numerical thresholds. The limitations of such an approach may result in a failure to capture the intricate relations between diagnostic tests and the varying prevalence of diseases. To explore this further, we have developed a freely available specialized computational tool that employs Bayesian inference to calculate the posterior probability of disease diagnosis. This novel software comprises of three distinct modules, each designed to allow users to define and compare parametric and nonparametric distributions effectively. The tool is equipped to analyze datasets generated from two separate diagnostic tests, each performed on both diseased and nondiseased populations. We demonstrate the utility of this software by analyzing fasting plasma glucose, and glycated hemoglobin A1c data from the National Health and Nutrition Examination Survey. Our results are validated using the oral glucose tolerance test as a reference standard, and we explore both parametric and nonparametric distribution models for the Bayesian diagnosis of diabetes mellitus.

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Section: Introductionmentioning

confidence: 99%