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Physical Sciences Forum, Volume 9, Issue 1

MaxEnt 2023 2023 - 27 articles

The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering

Garching, Germany | 3–7 July 2023

Volume Editors:
Roland Preuss, Max-Planck-Institut for Plasmaphysics, Germany
Udo von Toussaint, Max-Planck-Institut for Plasmaphysics, Germany

Cover Story: The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering continued a long series of MaxEnt-Workshops that started in the late 1970s of the previous century and centered on ill-conditioned data analysis tasks, thus making this workshop series one of the oldest (if not the oldest) conferences focusing on areas that are now commonly (but not always correctly) denoted as ML/AI. MaxEnt 2023 strived to present Bayesian inference and maximum entropy methods in data analysis, information processing, and inverse problems from a broad range of diverse disciplines, including astronomy and astrophysics, geophysics, medical imaging, acoustics, molecular imaging and genomics, non-destructive evaluation, particle and quantum physics, physical and chemical measurement techniques, economics, econometrics and robust estimation.
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Articles (27)

  • Proceeding Paper
  • Open Access
1,500 Views
9 Pages

Quantum Measurement and Objective Classical Reality

  • Vishal Johnson,
  • Philipp Frank and
  • Torsten Enßlin

We explore quantum measurement in the context of Everettian unitary quantum mechanics and construct an explicit unitary measurement procedure. We propose the existence of prior correlated states that enable this procedure to work and therefore argue...

  • Proceeding Paper
  • Open Access
1 Citations
2,711 Views
13 Pages

Bayesian Inference and Deep Learning for Inverse Problems

  • Ali Mohammad-Djafari,
  • Ning Chu,
  • Li Wang and
  • Liang Yu

Inverse problems arise anywhere we have an indirect measurement. In general, they are ill-posed to obtain satisfactory solutions, which needs prior knowledge. Classically, different regularization methods and Bayesian inference-based methods have bee...

  • Proceeding Paper
  • Open Access
1,797 Views
11 Pages

It has previously been shown that prior physics knowledge can be incorporated into the structure of an artificial neural network via neural activation functions based on (i) the correspondence under the infinite-width limit between neural networks an...

  • Proceeding Paper
  • Open Access
1,311 Views
9 Pages

Quantification of Endothelial Cell Migration Dynamics Using Bayesian Data Analysis

  • Anselm Hohlstamm,
  • Andreas Deussen,
  • Stephan Speier and
  • Peter Dieterich

Endothelial cells keep a tight and adaptive inner cell layer in blood vessels. Thereby, the cells develop complex dynamics through integrating active individual and collective cell migration, cell-cell interactions as well as interactions with extern...

  • Proceeding Paper
  • Open Access
2 Citations
1,687 Views
10 Pages

Proximal Nested Sampling with Data-Driven Priors for Physical Scientists

  • Jason D. McEwen,
  • Tobías I. Liaudat,
  • Matthew A. Price,
  • Xiaohao Cai and
  • Marcelo Pereyra

Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging. The framework is suitable for models with a log-convex likelihood, which are ubiquitous in the imaging s...

  • Proceeding Paper
  • Open Access
1,351 Views
10 Pages

Variational Bayesian Approximation (VBA) is a fast technique for approximating Bayesian computation. The main idea is to assess the joint posterior distribution of all the unknown variables with a simple expression. Mean–Field Variational Bayes...

  • Proceeding Paper
  • Open Access
1,379 Views
9 Pages

The ALPHA-g experiment at CERN intends to observe the effect of gravity on antihydrogen. In ALPHA-g, antihydrogen is confined to a magnetic trap with an axis aligned parallel to the Earth’s gravitational field. An imposed difference in the magn...

  • Proceeding Paper
  • Open Access
1 Citations
1,860 Views
10 Pages

Learned Harmonic Mean Estimation of the Marginal Likelihood with Normalizing Flows

  • Alicja Polanska,
  • Matthew A. Price,
  • Alessio Spurio Mancini and
  • Jason D. McEwen

Computing the marginal likelihood (also called the Bayesian model evidence) is an important task in Bayesian model selection, providing a principled quantitative way to compare models. The learned harmonic mean estimator solves the exploding variance...

  • Proceeding Paper
  • Open Access
1,137 Views
9 Pages

Bayesian Model Selection and Parameter Estimation for Complex Impedance Spectroscopy Data of Endothelial Cell Monolayers

  • Franziska Zimmermann,
  • Frauke Viola Härtel,
  • Anupam Das,
  • Thomas Noll and
  • Peter Dieterich

Endothelial barrier function can be quantified by the determination of the transendothelial resistance (TER) via impedance spectroscopy. However, TER can only be obtained indirectly based on a mathematical model. Models usually comprise a sequence of...

  • Proceeding Paper
  • Open Access
1 Citations
1,563 Views
8 Pages

Improving Inferences about Exoplanet Habitability

  • Risinie D. Perera and
  • Kevin H. Knuth

Assessing the habitability of exoplanets (planets orbiting other stars) is of great importance in deciding which planets warrant further careful study. Planets in the habitable zones of stars like our Sun are sufficiently far away from the star so th...

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Phys. Sci. Forum - ISSN 2673-9984