Inference of emission rates from multiple sources using Bayesian probability theory
Related Experiment Video
Updated: Jun 13, 2026

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
Published on: January 30, 2018
1Defence R&D Canada - Suffield, P.O. Box 4000 Stn Main, Medicine Hat, Alberta, CanadaT1A 8K6. eugene.yee@drdc-rddc.gc.ca
Abstract:
The determination of atmospheric emission rates from multiple sources using inversion (regularized least-squares or best-fit technique) is known to be very susceptible to measurement and model errors in the problem, rendering the solution unusable. In this paper, a new perspective is offered for this problem: namely, it is argued that the problem should be addressed as one of inference rather than inversion. Towards this objective, Bayesian probability theory is used to estimate the emission rates from multiple sources. The posterior probability distribution for the emission rates is derived, accounting fully for the measurement errors in the concentration data and the model errors in the dispersion model used to interpret the data. The Bayesian inferential methodology for emission rate recovery is validated against real dispersion data, obtained from a field experiment involving various source-sensor geometries (scenarios) consisting of four synthetic area sources and eight concentration sensors. The recovery of discrete emission rates from three different scenarios obtained using Bayesian inference and singular value decomposition inversion are compared and contrasted.
Related Concept Videos
Propagation of Uncertainty from Systematic Error
Poisson Probability Distribution
The...
Propagation of Uncertainty from Random Error
Theory of Attribution I: Correspondent Inference Theory
Probability in Statistics
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
Probability Distributions
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson probability...

