NOTICE: Due to a lapse in annual appropriations, most of this website is not being updated. Learn more.

Form submissions will still be accepted but will not receive responses at this time. Sections of this site for programs using non-appropriated funds (such as NVLAP) or those that are excepted from the shutdown (such as CHIPS and NVD) will continue to be updated.

U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

PUBLICATIONS

Optimal Bayesian Experimental Design

Published

Author(s)

Robert D. McMichael

Abstract

This manual describes an implementation of optimal Bayesian experimental design methods. These methods address routine measurements where data are fit to experimental models in order to obtain model parameters. The twin benefits of these methods are reduced uncertainty with fewer required measurements. These methods are therefore most beneficial in measurements where measurements are expensive in terms of money, time, risk, labor and/or discomfort. The price for these benefits lies in the complexity of automating such measurements and in the computational load required. It is the goal of this package to assist potential users in overcoming at least the programming hurdles.
Citation
Github
Pub Weblink
http://www.github.com
Pub Type
Websites

Download Paper

Local Download

Keywords

Bayesian, GitHub, measurement
Experiment design, Magnetics and Quantum information science

Citation

McMichael, R. (2019), Optimal Bayesian Experimental Design, Github, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=928064, http://www.github.com (Accessed October 14, 2025)

Issues

If you have any questions about this publication or are having problems accessing it, please contact [email protected].

Created September 4, 2019, Updated January 27, 2020
Was this page helpful?