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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 ...
Bzdok, Danilo +2 more
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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 in Official Statistics
CoRR, 2018 In the first half of 2018, the Federal Statistical Office of Germany (Destatis) carried out a "Proof of Concept Machine Learning" as part of its Digital Agenda. A major component of this was surveys on the use of machine learning methods in official statistics, which were conducted at selected national and international statistical institutions and ...
Martin Beck +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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Machine Learning Models for Statistical Analysis
The International Arab Journal of Information Technology, 2023 Compared to traditional statistical models, Machine Learning (ML) algorithms provide the ability to interpret, understand and summarize patterns and regularities in observed data for making predictions in an advanced and more sophisticated way. The main reasons for the advantage of ML methods in making predictions are a small number of significant ...
Marko Grebovic +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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Machine learning renormalization group for statistical physics
Machine Learning: Science and Technology, 2023 Abstract We develop a machine-learning renormalization group (MLRG) algorithm to explore and analyze many-body lattice models in statistical physics. Using the representation learning capability of generative modeling, MLRG automatically learns the optimal renormalization group (RG) transformations from self-generated spin configurations
Wanda Hou, Yi-Zhuang You
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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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Algorithmic statistics, prediction and machine learning
CoRR, 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. In this paper we extend this framework in two directions.
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Machine Learning as Statistical Data Assimilation
CoRR, 2017 We identify a strong equivalence between neural network based machine learning (ML) methods and the formulation of statistical data assimilation (DA), known to be a problem in statistical physics. DA, as used widely in physical and biological sciences, systematically transfers information in observations to a model of the processes producing the ...
Henry D. I. Abarbanel +2 more
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