Special Issue "MaxEnt 2018 - The 38th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering"

A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: closed (30 November 2018).

Special Issue Editor

Prof. Grigorios A. Pavliotis
Website
Guest Editor
Applied Mathematics and Mathematical Physics Section, Department of Mathematics, Imperial College London, London, UK
Interests: analysis, numeric and statistical inference for multiscale stochastic systems; non-equilibrium statistical mechanics; homogenization theory for PDEs and SDEs

Special Issue Information

Dear Colleague,

For over 37 years, the Max Ent workshops have explored the use of Bayesian and Maximum Entropy methods in scientific and engineering applications. The workshop invites contributions on all aspects of probabilistic inference, including novel techniques and applications, and work that sheds new light on the foundations of inference. Areas of application have included astronomy and astrophysics, chemistry, communications theory, cosmology, climate studies, earth science, fluid mechanics, genetics, geophysics, machine learning, material science, medical imaging, nanoscience, source separation, thermodynamics (equilibrium and non-equilibrium), particle physics, plasma physics, quantum mechanics, robotics and social sciences. Bayesian computational techniques, such as Markov chain Monte Carlo sampling have been regular topics, as are approximate inferential methods. Foundational issues involving probability theory and information theory, and the novel application of inference to illuminate the foundations of physical theories, have also been of keen interest.

Participants are welcome to submit an extended version of the papers from the MaxEnt 2018 Workshop to this Special Issue.

Prof. Grigorios A. Pavliotis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

Open AccessArticle
Bayesian Analysis of Femtosecond Pump-Probe Photoelectron-Photoion Coincidence Spectra with Fluctuating Laser Intensities
Entropy 2019, 21(1), 93; https://doi.org/10.3390/e21010093 - 19 Jan 2019
Cited by 1
Abstract
This paper employs Bayesian probability theory for analyzing data generated in femtosecond pump-probe photoelectron-photoion coincidence (PEPICO) experiments. These experiments allow investigating ultrafast dynamical processes in photoexcited molecules. Bayesian probability theory is consistently applied to data analysis problems occurring in these types of experiments [...] Read more.
This paper employs Bayesian probability theory for analyzing data generated in femtosecond pump-probe photoelectron-photoion coincidence (PEPICO) experiments. These experiments allow investigating ultrafast dynamical processes in photoexcited molecules. Bayesian probability theory is consistently applied to data analysis problems occurring in these types of experiments such as background subtraction and false coincidences. We previously demonstrated that the Bayesian formalism has many advantages, amongst which are compensation of false coincidences, no overestimation of pump-only contributions, significantly increased signal-to-noise ratio, and applicability to any experimental situation and noise statistics. Most importantly, by accounting for false coincidences, our approach allows running experiments at higher ionization rates, resulting in an appreciable reduction of data acquisition times. In addition to our previous paper, we include fluctuating laser intensities, of which the straightforward implementation highlights yet another advantage of the Bayesian formalism. Our method is thoroughly scrutinized by challenging mock data, where we find a minor impact of laser fluctuations on false coincidences, yet a noteworthy influence on background subtraction. We apply our algorithm to data obtained in experiments and discuss the impact of laser fluctuations on the data analysis. Full article
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