Results 41 to 50 of about 15,452 (256)
Understanding Hierarchical Processes
Entropy, 2022 Hierarchical stochastic processes, such as the hierarchical Dirichlet process, hold an important position as a modelling tool in statistical machine learning, and are even used in deep neural networks.
Wray Buntine
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Contributions to the Theory of Dirichlet Processes
The Annals of Probability, 1973 Abstract : The authors derive some basic properties of a sample X(1),...,X(n) from a Dirichlet process. Let r(i) = 0 if X(i) = X(k) for some k = 1, ..., i-1, and 1 otherwise. They authors first establish the distribution of the summation from i=1 to n of r(i), the number of distinct observations in the sample, and certain conditional and unconditional ...
Korwar, Ramesh M., Hollander, Myles
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New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design
Advanced Engineering Materials, EarlyView.This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare +5 more
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Mathematics, 2022 The extreme value theory is widely used in economic and environmental domains, it aims to study the stochastic extreme behaviors associated with rare events.
Yingjie Wang, Xinsheng Liu
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Note on Convergence of Dirichlet Processes
Bulletin of the London Mathematical Society, 1993 The authors study the limit behaviour of a sequence \(\{P_ n\}\) of distributions on the path space \(C([0,\infty), \mathbb{R}^ d)\) where the definition of each \(P_ n\) involves a measurable function \(\varphi_ n\) on \(\mathbb{R}^ d\) and a conservative diffusion process associated with a peculiar Dirichlet form.
Lyons, T, Zhang, T
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High‐Resolution Corrosion Fingerprinting of Alloy Libraries via Microdroplet Spectroelectrochemistry
Advanced Engineering Materials, EarlyView.Ionic‐liquid microdroplet spectroelectrochemistry enables stable, localized electrochemical measurements over extended timescales. By coupling impedance spectroscopy, wetting analysis, finite‐element simulations, and operando Raman spectroscopy, it disentangles geometrical and electrochemical effects, opening new opportunities for rapid corrosion ...
Ekaterina Kurchavova +3 more
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Location Dependent Dirichlet Processes [PDF]
, 2017 Dirichlet processes (DP) are widely applied in Bayesian nonparametric modeling. However, in their basic form they do not directly integrate dependency information among data arising from space and time. In this paper, we propose location dependent Dirichlet processes (LDDP) which incorporate nonparametric Gaussian processes in the DP modeling framework
Shiliang Sun +2 more
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A note on the implementation of hierarchical dirichlet processes [PDF]
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers on - ACL-IJCNLP '09, 2009 The implementation of collapsed Gibbs samplers for non-parametric Bayesian models is non-trivial, requiring considerable book-keeping. Goldwater et al. (2006a) presented an approximation which significantly reduces the storage and computation overhead, but we show here that their formulation was incorrect and, even after correction, is grossly ...
Blunsom, Phil +3 more
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Advanced Functional Materials, EarlyView.HfxZr1−xO2${\rm Hf}_x{\rm Zr}_{1-x}{\rm O}_2$ offers CMOS‐compatible nanoscale ferroelectricity yet suffers from a high Ec${\rm E}_c$ demanding large operating voltages. A unified phase‐field framework spanning AFE/FE/DE phases shows how FE grains soften neighboring AFE grains over λ$\lambda$ ≈$\approx$ 22–37 nm.
P. Pankaj +4 more
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Bayesian Dependence Tests for Continuous, Binary and Mixed Continuous-Binary Variables
Entropy, 2016 Tests for dependence of continuous, discrete and mixed continuous-discrete variables are ubiquitous in science. The goal of this paper is to derive Bayesian alternatives to frequentist null hypothesis significance tests for dependence.
Alessio Benavoli, Cassio P. de Campos
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