Skip to main navigation
Skip to search
Skip to main content
Oak Ridge National Laboratory Home
Help & FAQ
Home
Profiles
Organizations
Projects
Publications
Datasets
Awards
Engagement
Search by expertise, name or affiliation
Bayesian inference for plasmonic nanometrology
Joseph M. Lukens
,
Ali Passian
Quantum Sensing and Computing
Research output
:
Contribution to journal
›
Article
›
peer-review
5
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Bayesian inference for plasmonic nanometrology'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Mathematics
Bayesian Inference
100%
Bayesian
50%
Markov Chain Monte Carlo
50%
Bayesian Model Comparison
50%
Light Particle
50%
Bayesian Estimate
50%
Model Selection
50%
Uncertainty Quantification
50%
Parameter Estimate
50%
Sampling Technique
50%
Observed Data
50%
Engineering
Nanoparticle
100%
Plasmonics
100%
Nanometrology
100%
Uncertainty Quantification
33%
Applicability
33%
Observed Data
33%
Scatterer
33%
Geometric Shape
33%
Monte Carlo Sampling Technique
33%
Parameter Estimate
33%
Field Problem
33%
Material Science
Nanoparticle
100%
Markov Chain Monte Carlo
33%