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Related Experiment Video

Updated: Aug 5, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Prediction-based uncertainty quantification for exchangeable sequences.

Sandra Fortini¹, Sonia Petrone¹

¹Department of Decision Sciences, Bocconi University, via Roentgen 1, 20136 Milano, Italy.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences

|March 27, 2023

Summary

This summary is machine-generated.

Bayesian statistics and machine learning now prioritize prediction over inference. This study shows uncertainty, like credible intervals, stems from prediction, offering new insights into predictive efficiency and learning rules.

Keywords:

Bayesian prediction almost sure conditional convergence approximation of Bayesian procedures asymptotic normality credible intervals martingales

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Area of Science:

Statistics
Machine Learning
Bayesian Inference

Background:

Prediction is central to Bayesian statistics and machine learning, shifting focus from traditional inference.
Exchangeability in the Bayesian approach frames uncertainty through prediction.

Purpose of the Study:

To demonstrate how posterior distributions and credible intervals in Bayesian statistics can be understood via prediction.
To explore the relationship between predictive rules, learning, and frequentist coverage.

Main Methods:

Analyzing the posterior law's relationship to the predictive distribution.
Proving the marginal asymptotic Gaussianity of the posterior distribution.
Investigating the role of predictive updates in incorporating new observations.

Main Results:

The posterior distribution is centered on the predictive distribution.
Asymptotic variance depends on predictive updates, detailing how information is incorporated.
Credible intervals can be derived solely from the predictive rule, independent of specific models or priors.

Conclusions:

Uncertainty in Bayesian statistics is fundamentally linked to prediction.
This perspective offers insights into frequentist coverage and predictive learning.
Introduces the concept of predictive efficiency, suggesting avenues for future research.