Real-time data analysis via approximate inference in next-generation spectroscopic and experimental systems

T. J. Huffman; Robert Furstenberg; Christopher J. Breshike; Christopher A. Kendziora; R. Andrew McGill

doi:10.1117/12.3013830

7 June 2024 Real-time data analysis via approximate inference in next-generation spectroscopic and experimental systems

T. J. Huffman, Robert Furstenberg, Christopher J. Breshike, Christopher A. Kendziora, R. Andrew McGill

Proceedings Volume 13031, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX; 130310F (2024) https://doi.org/10.1117/12.3013830
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

The next generation of infrared spectroscopic solutions collect a massive amount of data that is realistically much too dense to be intuitively understood by a human. Thus, as a practical necessity, the user is generally interested in a smaller number of “latent” variables that aren’t directly observed. However, the usual method of considering a more manageable subset of the raw data throws away a great deal of collected information. The problem of distilling the latent variables and related uncertainties from the raw data is one of statistical inference. We adopt a Bayesian approach to better quantify the uncertainties in the latent variables. While our prior work has focused on exact inference methods such as Gibbs Sampling and Hamiltonian Monte-Carlo, we have begun exploring techniques for approximate inference, which allow such data analysis to proceed in quasi real-time.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

T. J. Huffman, Robert Furstenberg, Christopher J. Breshike, Christopher A. Kendziora, and R. Andrew McGill "Real-time data analysis via approximate inference in next-generation spectroscopic and experimental systems", Proc. SPIE 13031, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX, 130310F (7 June 2024); https://doi.org/10.1117/12.3013830

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