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Statistics versus Machine Learning [PDF]
Nature Methods, 2018 Two major goals in the study of biological systems are inference andprediction. Inference creates a mathematical model of the datageneration process to formalize our understanding or test ahypothesis about how the system behaves. Prediction aims atforecasting unobserved outcomes or future behavior, such as whethera mouse with a given phenotype will ...
Danilo Bzdok +2 more
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Algorithmic statistics, prediction and machine learning [PDF]
, 2015 Algorithmic statistics considers the following problem: given a binary string $x$ (e.g., some experimental data), find a "good" explanation of this data. It uses algorithmic information theory to define formally what is a good explanation.
Milovanov, Alexey
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Statistics for Machine Learning with Mathematica Applications
, 2023 In recent years, the field of statistics has experienced a surge in interest and application, largely due to significant advances in computer technology. This progress has led to remarkable developments in statistics methods and algorithms, enabling their widespread adoption across various disciplines.
M. M. Hammad
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Representativeness in Statistics, Politics, and Machine Learning [PDF]
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 2021 Representativeness is a foundational yet slippery concept. Though familiar at first blush, it lacks a single precise meaning. Instead, meanings range from typical or characteristic, to a proportionate match between sample and population, to a more general sense of accuracy, generalizability, coverage, or inclusiveness.
Kyla Chasalow, Karen Levy
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Machine learning and statistical physics: preface
Journal of Physics A: Mathematical and Theoretical, 2020 The aim of this special issue is to provide a picture of the state-of-the-art and open challenges in machine learning from a statistical physics perspective, mainly that of disordered systems.
Adriano Barra +4 more
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Statistical Machine Learning for Human Behaviour Analysis [PDF]
Entropy, 2020 Human behaviour analysis has introduced several challenges in various fields, such as applied information theory, affective computing, robotics, biometrics and pattern recognition [...]
Thomas B. Moeslund +4 more
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Statistics of Solar Wind Electron Breakpoint Energies Using Machine Learning Techniques [PDF]
, 2020 Solar wind electron velocity distributions at 1 au consist of a thermal "core" population and two suprathermal populations: "halo" and "strahl". The core and halo are quasi-isotropic, whereas the strahl typically travels radially outwards along the ...
Bakrania, Mayur R. +6 more
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A statistical and machine learning approach to the study of astrochemistry
Faraday Discussions, 2023 We use Bayesian inference together with the MOPED compression algorithm to help determine which species should be prioritised for future detections in order to better constrain the values of binding energies in the ISM.
Johannes Heyl +2 more
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Proximal Algorithms in Statistics and Machine Learning
Statistical Science, 2015 In this paper we develop proximal methods for statistical learning. Proximal point algorithms are useful in statistics and machine learning for obtaining optimization solutions for composite functions. Our approach exploits closed-form solutions of proximal operators and envelope representations based on the Moreau, Forward-Backward, Douglas-Rachford ...
Polson, Nicholas G. +2 more
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Fighting Money Laundering With Statistics and Machine Learning
IEEE Access, 2023 Accepted for publication in IEEE Access, vol. 11, pp.
Rasmus Ingemann Tuffveson Jensen +1 more
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