Results 21 to 30 of about 40,175 (220)
Advanced Science, EarlyView.This perspective proposes a cohesive machine learning strategy to decode microplastic aging. It advocates for Federated Learning to dismantle global data silos and introduces the TRACE framework (TRansport, Aging, Corona, Ecotoxicity). By integrating physics‐informed modeling with causal discovery, this approach bridges the laboratory‐field gap to ...
Yaping Lyu +6 more
wiley +1 more source
Advanced Energy Materials, EarlyView.In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu +6 more
wiley +1 more source
On Bayes' theorem and the inverse Bernoulli theorem
, 1988 A recent assertion by S. M. Stigler that Thomas Bayes was perhaps anticipated in the discovery of the result that today bears his name is exposed to further scrutiny here.
Dale, A.I, A.I Dale
core +1 more source
Exact Bayes’ theorem based probabilistic data association for iterative MIMO detection and decoding [PDF]
, 2013 In our previous work, it was shown that the conventional approximate Bayes' theorem based probabilistic data association (PDA) algorithms output "nominal APPs", which are unsuitable for the classic architecture of iterative detection and decoding (IDD ...
Yang, Shaoshi +3 more
core +1 more source
The Geography of Success: A Spatial Analysis of Export Intensity in the Italian Wine Industry
Agribusiness, EarlyView.ABSTRACT This paper investigates the paradox of how Italy's fragmented, SME‐dominated wine industry achieves global export success. Moving beyond purely firm‐centric explanations, we test whether export intensity is spatially dependent, clustering geographically in regional ecosystems.
Nicolas Depetris Chauvin, Jonas Di Vita
wiley +1 more source
Advanced Intelligent Systems, EarlyView.This paper presents a lidar‐based sensor node design and a rule‐based state observer for edge‐based traffic participant tracking. Unlike other state‐of‐the‐art methods, this state observer enables real‐time, CPU‐only edge processing without relying on machine learning approaches.
Simon Schäfer +2 more
wiley +1 more source
Bayes’ Theorem and Naive Bayes Classifier
, 2019 The goal of this article is to give a mathematically rigorous yet easily accessible introduction to Bayes’ theorem and the foundations of naive Bayes learning. Starting from fundamental elements of probability theory, this text outlines all steps leading
Berrar, Daniel, Daniel Berrar
core +1 more source
Teaching Bayes’ Theorem: Strength of Evidence as Predictive Accuracy [PDF]
, 2017 Although teaching Bayes’ theorem is popular, the standard approach—targeting posterior distributions of parameters—may be improved. We advocate teaching Bayes’ theorem in a ratio form where the posterior beliefs relative to the prior beliefs equals the ...
Richard D. Morey +3 more
core +1 more source
Advances in causal discovery methods for ecological time series
Biological Reviews, EarlyView.ABSTRACT Recent advances in data collection technologies (e.g. automated sensor networks, satellite remote sensing, and high‐throughput sequencing) have greatly expanded the availability of ecological time series, enabling new opportunities for causal analyses in dynamic ecosystems.
Kenta Suzuki +6 more
wiley +1 more source
Explaining the Bayes' theorem graphically [PDF]
, 2018 The Bayes’ theorem on conditional probabilities is normally presented to students in introductory courses/modules on Statistics and Probability. This because most STEM students will make use of conditional probabilities in their professional lives with ...
Pablo Ballesteros-Perez (4862347) +2 more
core +1 more source