Results 51 to 60 of about 1,536,761 (277)
Stationary Exponential Families
The Annals of Statistics, 1995 An exponential family for stationary sequences of random vectors in \(\mathbb{R}^ d\) is defined by making use of the Ionesco Tulcea theorem [\textit{C. T. Ionesco Tulcea}, Atti Accad. Naz. Lincei, Rend., Cl. Sci. Fis. Mat. Natur., VIII. S. 7, 208-211 (1950; Zbl 0035.152)].
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Advanced Engineering Materials, Volume 27, Issue 6, March 2025.The stability criteria affecting the formation of high‐entropy alloys, particularly focusing in supersaturated solid solutions produced by mechanical alloying, are analyzed. Criteria based on Hume–Rothery rules are distinguished from those derived from thermodynamic relations. The formers are generally applicable to mechanically alloyed samples.
Javier S. Blázquez +5 more
wiley +1 more source
Clustering above Exponential Families with Tempered Exponential Measures [PDF]
, 2022 The link with exponential families has allowed $k$-means clustering to be generalized to a wide variety of data generating distributions in exponential families and clustering distortions among Bregman divergences.
Nock, Richard +2 more
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Group Invariance of Information Geometry on q-Gaussian Distributions Induced by Beta-Divergence
Entropy, 2013 We demonstrate that the q-exponential family particularly admits natural geometrical structures among deformed exponential families. The property is the invariance of structures with respect to a general linear group, which transitively acts on the space
Shinto Eguchi, Atsumi Ohara
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Journal of Biostatistics and Epidemiology, 2023 Many experts in the field of distribution theory have focused on extending probability distributions utilizing extended families of continuous distributions to improve the modeling adaptability of the conventional probability distributions.
Ibrahim Sule +2 more
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Likelihood Ratio Exponential Families
CoRR, 2020 The exponential family is well known in machine learning and statistical physics as the maximum entropy distribution subject to a set of observed constraints, while the geometric mixture path is common in MCMC methods such as annealed importance sampling.
Rob Brekelmans +4 more
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Advanced Engineering Materials, EarlyView.Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch +3 more
wiley +1 more source
Entropy‐Driven Design of Low‐Melting‐Point Alloys via Compositionally Complex Strategy
Advanced Engineering Materials, EarlyView.Conventional low‐melting‐point alloys (LMPAs) are limited by a narrow compositional space and inherent property trade‐offs. This review presents an entropy‐driven design strategy that overcomes these limitations, ushering in a new class of low‐melting‐point compositionally complex alloys (LMCCAs).
Yinghui Shang +6 more
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Advanced Engineering Materials, EarlyView.Cu and combined Cu–P microalloying refine the microstructure and enhance the nanoindentation‐derived fracture resistance of CoNiAlSi ferromagnetic shape memory alloys without suppressing the martensitic transformation. Comparative SEM, DSC, and nanoindentation results reveal that the CuP‐containing alloy provides the most balanced response, achieving ...
Mehmet Demir
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Kernel methods and the exponential family [PDF]
Neurocomputing, 2006 The success of support vector machine (SVM) has given rise to the development of a new class of theoretically elegant learning machines which use a central concept of kernels and the associated reproducing kernel Hilbert space (RKHS). Exponential families, a standard tool in statistics, can be used to unify many existing machine learning algorithms ...
Stéphane Canu, Alexander J. Smola
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