
Bayesian approach to analyzing holograms of colloidal particles
Author(s) -
Thomas G. Dimiduk,
Vinothan N. Manoharan
Publication year - 2016
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.24.024045
Subject(s) - holography , optics , markov chain monte carlo , bayesian probability , monte carlo method , bayesian inference , computer science , digital holography , tracking (education) , refractive index , particle (ecology) , physics , mathematics , artificial intelligence , statistics , psychology , pedagogy , oceanography , geology
We demonstrate a Bayesian approach to tracking and characterizing colloidal particles from in-line digital holograms. We model the formation of the hologram using Lorenz-Mie theory. We then use a tempered Markov-chain Monte Carlo method to sample the posterior probability distributions of the model parameters: particle position, size, and refractive index. Compared to least-squares fitting, our approach allows us to more easily incorporate prior information about the parameters and to obtain more accurate uncertainties, which are critical for both particle tracking and characterization experiments. Our approach also eliminates the need to supply accurate initial guesses for the parameters, so it requires little tuning.