Preface of the 41st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering
- Ali Mohammad-Djafari (CNRS, France)—Bayesian and Machine Learning Methods for Inverse Problem;
- Kevin H. Knuth (University at Albany, USA)—Why Mathematics Works and Why Physics is Mathematical;
- John Skilling (University of Cambridge, UK)—Foundations;
- Frank Nielsen (Sony CSL, Japan)—Introduction to Information Geometry;
- Fréderic Barbaresco (THALES, France)—Symplectic Theory of Heat and Information based on Souriau Lie Groups Thermodynamics, Coquinot Thermodynamic Dissipative Bracket and Sabourin Transverse Poisson Structures: Applications to Lindblad Equation;
- Ariel Caticha (University at Albany, USA)—Entropic Dynamics and Quantum Measurement.
- Anna Simoni (ENSAE, France)—Bayesian Exponentially Tilted Empirical Likelihood to Endogeneity Testing;
- Antoine Bourget (CEA and ENS Paris, France)—The Geometry of Quivers;
- Bobak Toussi Kiani (MIT, USA)—Quantum Algorithms for Group Convolution, Cross-Correlation, and Equivariant Transformations;
- Emtiyaz Khan (RIKEN, Japan)—The Bayesian Learning Rule;
- Fabrizia Guglielmetti (ALMA Regional Center Scientist at European Southern Observatory, Germany)—Bayesian and Machine Learning Methods in the Big Data Era for Astronomical Imaging;
- Livia Partay (University of Warwick, UK)—Nested Sampling for Materials;
- Lorenzo Valzania (LKB: Sorbonne University-ENS-Collège de France, France)—Imaging Behind Scattering Layers;
- Pierre-Henri Wuillemin (Laboratoire d’Informatique de Paris, France)—Learning Continuous High-Dimensional Models using Mutual Information and Copula Bayesian Networks;
- Torsten Ensslin (MPA, Germany)—Theoretical Modeling of Communication Dynamics;
- Will Handley (University of Cambridge, UK)—Bayesian Sparse Reconstruction: a Brute-Force Approach to Astronomical Imaging and Machine Learning;
- Piotr Graczyk (Angers, France)—Graphical Gaussian Models Associated to a Homogeneous Graph with Permutation Symmetries;
- Olivier Rioul (Telecom ParisTech)—What is Randomness? The Interplay between Alpha Entropies, Total Variation, and Guessing.
- The foundations of probability, inference, information, and entropy;
- Bayesian physics, informed thermodynamics, and informed machine learning;
- Information theory and machine learning tools for inverse problems;
- Bayesian and maximum entropy in real-world applications;
- Geometric statistical mechanics/physics and Lie groups;
- Thermodynamics and maximum entropy densities;
- Quantum theory, computation, tomography, and applications.
Conflicts of Interest
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Barbaresco, F.; Mohammad-Djafari, A.; Nielsen, F.; Trassinelli, M. Preface of the 41st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. Phys. Sci. Forum 2022, 5, 43. https://doi.org/10.3390/psf2022005043
Barbaresco F, Mohammad-Djafari A, Nielsen F, Trassinelli M. Preface of the 41st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. Physical Sciences Forum. 2022; 5(1):43. https://doi.org/10.3390/psf2022005043Chicago/Turabian Style
Barbaresco, Frédéric, Ali Mohammad-Djafari, Frank Nielsen, and Martino Trassinelli. 2022. "Preface of the 41st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering" Physical Sciences Forum 5, no. 1: 43. https://doi.org/10.3390/psf2022005043