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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
semanticscholar +9 more sources
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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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 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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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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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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Application of statistical machine learning in biomarker selection
Scientific Reports, 2023 AbstractIn the recent JAVELIN Bladder 100 phase 3 trial, avelumab plus best supportive care significantly prolonged overall survival relative to best supportive care alone as first-line maintenance therapy following first-line platinum-based chemotherapy in patients with advanced urothelial cancer (aUC).
Ritwik Vashistha +4 more
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Machine Learning in Official Statistics
, 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 ...
Beck, Martin +2 more
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