Results 71 to 80 of about 52,951 (307)

Probability density estimation with tunable kernels using orthogonal forward regression [PDF]

, 2010
A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimation process determines a tunable kernel, namely, its center vector and ...
Hong, Xia, Chen, Sheng, Harris, Chris J., Chen, S.   +3 more
core   +1 more source

An Experimental High‐Throughput Approach for the Screening of Hard Magnet Materials

Advanced Engineering Materials, EarlyView.
An entire workflow for the high‐throughput characterization and analysis of compositionally graded magnetic films is presented. Characterization protocols, data management tools and data analysis approaches are illustrated with test case Sm(Fe, V)12 based films.
William Rigaut, Pierre Le Berre, Hamdi Jaballah, Lukas Fink, Victor Poline, Richard Haettel, Stéphane Grenier, Gilbert Chahine, Ewen Bellec, Guillaume Buatier, Wilfried Hortschitz, Santa Pile, Thomas Schrefl, Nils Blanc, Yuan Hong, Thibaut Devillers, Nora M. Dempsey   +16 more
wiley   +1 more source

Distributional CNN-LSTM, KDE, and Copula Approaches for Multimodal Multivariate Data: Assessing Conditional Treatment Effects

Analytics
We introduce a distributional CNN-LSTM framework for probabilistic multivariate modeling and heterogeneous treatment effect (HTE) estimation. The model jointly captures complex dependencies among multiple outcomes and enables precise estimation of ...
Jong-Min Kim
doaj   +1 more source

Improved Minimum Entropy Filtering for Continuous Nonlinear Non-Gaussian Systems Using a Generalized Density Evolution Equation

Entropy, 2013
This paper investigates the filtering problem for multivariate continuous nonlinear non-Gaussian systems based on an improved minimum error entropy (MEE) criterion.
Jinliang Xu, Guolian Hou, Jianhua Zhang, Fang Fang, Mifeng Ren   +4 more
doaj   +1 more source

Machine Learning‐Assisted Inverse Design of Soft and Multifunctional Hybrid Liquid Metal Composites

Advanced Functional Materials, EarlyView.
A machine learning framework is presented for inverse design of synthesizable multifunctional composites containing both liquid metal and solid inclusions. By integrating physics‐based modeling, data‐driven prediction, and Bayesian optimization, the approach enables intelligent design of experiments to identify optimal compositions and realize these ...
Lijun Zhou, Yunsik Ohm, Ren‐Mian Chin, Olivia Kerr, Krithika Manohar, Mohammad H. Malakooti   +5 more
wiley   +1 more source

Comparative Evaluation of Nonparametric Density Estimators for Gaussian Mixture Models with Clustering Support

Axioms
The article investigates the accuracy of nonparametric univariate density estimation methods applied to various Gaussian mixture models. A comprehensive comparative analysis is performed for four popular estimation approaches: adaptive kernel density ...
Tomas Ruzgas, Gintaras Stankevičius, Birutė Narijauskaitė, Jurgita Arnastauskaitė Zencevičienė   +3 more
doaj   +1 more source

On Multivariate Density Estimates Based on Orthogonal Expansions

The Annals of Mathematical Statistics, 1970
The topics of orthogonality and Fourier series occupy a central position in analysis. Nevertheless, there is surprisingly little statistical literature, with the exception of that of time series and regression, which involves Fourier analysis. In the last decade however, several papers have appeared which deal with the estimation of orthogonal ...
Tarter, Michael, Kronmal, Richard
openaire   +3 more sources

Sustainable Electrochemical Synthesis of High‐Quality MXenes: Mechanistic Insights, Applications, Challenges, and Technological Prospects

Advanced Functional Materials, EarlyView.
Electrochemical etching provides an eco‐friendly alternative to hazardous HF methods for MXene production. This approach facilitates the selective isolation of the A‐layer from MAX phases with tunable surface terminations. Controlling voltage, electrolytes, temperature, and duration enables the optimal structural integrity. Nevertheless, existing scale
Jagdeep Singh, Madhusudhana M Devadiga, Santosh K. Tiwari, AS Dhaliwal, Gurpreet Singh Selopal   +4 more
wiley   +1 more source

Robust Bayesian Estimation of Mixed Normal Dirichlet Models to Study the Effect of Some Climatic Factors on Evaporation

Proceedings of the International Conference on Applied Innovations in IT
This study proposes and validates a robust Bayesian model based on a Dirichlet process mixture of normals (DMNM) for probability density estimation and missing data imputation in multivariate datasets.
Hassan Sami, Asmaa Ayoob
doaj   +1 more source

Polynomial Histograms for Multivariate Density and Mode Estimation

Scandinavian Journal of Statistics, 2012
Abstract.  We consider the problem of efficiently estimating multivariate densities and their modes for moderate dimensions and an abundance of data. We propose polynomial histograms to solve this estimation problem. We present first‐ and second‐order polynomial histogram estimators for a general d‐dimensional setting.
Jing, Junmei, Koch, Inge, Naito, Kanta
openaire   +2 more sources