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Related Concept Videos

Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
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Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
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Uncertainty estimation in simultaneous Bayesian tracking and environmental inversion.

Stan E Dosso1, Michael J Wilmut

  • 1School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia, Canada. sdosso@uvic.ca

The Journal of the Acoustical Society of America
|July 24, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian method for tracking acoustic sources and determining ocean environmental parameters simultaneously. It quantifies uncertainty in source position and environmental properties using advanced sampling techniques.

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Area of Science:

  • Ocean acoustics
  • Inverse problems
  • Bayesian inference

Background:

  • Accurate acoustic source localization and environmental characterization are crucial for underwater applications.
  • Traditional methods often struggle with unknown environmental parameters or source characteristics.

Purpose of the Study:

  • To develop a unified Bayesian framework for simultaneously solving acoustic source tracking and geoacoustic inversion problems.
  • To rigorously quantify parameter uncertainty, providing insights into data and prior information content.

Main Methods:

  • A Bayesian approach treating source and environmental parameters as random variables.
  • Utilizing noisy acoustic data and prior information on parameter constraints.
  • Implementing an efficient Markov-chain Monte Carlo (MCMC) importance-sampling method combining Metropolis and Gibbs sampling.

Main Results:

  • Marginal posterior probability densities (PPDs) for environmental parameters.
  • Joint marginal PPDs for source ranges and depths, illustrating resolved uncertainties.
  • Successful application to simulated scenarios of submerged source tracking and geoacoustic inversion.

Conclusions:

  • The developed Bayesian framework effectively addresses coupled inverse problems in ocean acoustics.
  • The MCMC approach provides robust uncertainty quantification for source and environmental parameters.
  • This method enhances understanding of information content in acoustic data for complex underwater scenarios.