Results 61 to 70 of about 1,242,716 (272)

BFDA: A MATLAB Toolbox for Bayesian Functional Data Analysis

Journal of Statistical Software, 2019
We provide a MATLAB toolbox, BFDA, that implements a Bayesian hierarchical model to smooth multiple functional data samples with the assumptions of the same underlying Gaussian process distribution, a Gaussian process prior for the mean function, and an ...
Jingjing Yang, Peng Ren
doaj   +1 more source

Surrogate modeling of waveform response using singular value decomposition and Bayesian optimization

Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2021
In the early stage of vehicle development, it is required to implement a target cascading study by solving inverse problems. However, simulation costs of vehicle dynamics to predict transient responses and frequency responses make the target cascading ...
Kohei SHINTANI, Takao FUJIMOTO, Masaaki OKAMOTO, Atsuji ABE, Yasushi YAMAMOTO   +4 more
doaj   +1 more source

Mind the nuisance: Gaussian process classification using privileged noise [PDF]

, 2014
The learning with privileged information setting has recently attracted a lot of attention within the machine learning community, as it allows the integration of additional knowledge into the training process of a classifier, even when this comes in the ...
Hernández-lobato, Daniel, Kersting, Kristian, Lampert, Christoph H, Quadrianto, Novi, Sharmanska, Viktoriia   +4 more
core   +1 more source

Fluid Biomarkers of Disease Burden and Cognitive Dysfunction in Progressive Supranuclear Palsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Identifying objective biomarkers for progressive supranuclear palsy (PSP) is crucial to improving diagnosis and establishing clinical trial and treatment endpoints. This study evaluated fluid biomarkers in PSP versus controls and their associations with regional 18F‐PI‐2620 tau‐PET, clinical, and cognitive outcomes.
Roxane Dilcher, Charles B. Malpas, Stuart J. McDonald, William T. O’Brien, Craig Despott, Kelly L. Bertram, Matthew P. Pase, Lukas Frontzkowski, Meng Law, Terence J. O’Brien, Lucy Vivash   +10 more
wiley   +1 more source

Gaussian process models for periodicity detection [PDF]

, 2016
We consider the problem of detecting and quantifying the periodic component of a function given noise-corrupted observations of a limited number of input/output tuples.
Durrande, Nicolas, Hensman, James, Lawrence, Neil D., Rattray, Magnus   +3 more
core   +2 more sources

Variations and estimators for the selfsimilarity order through Malliavin calculus

, 2009
Using multiple stochastic integrals and the Malliavin calculus, we analyze the asymptotic behavior of quadratic variations for a specific non-Gaussian self-similar process, the Rosenblatt process. We apply our results to the design of strongly consistent
Tudor, Ciprian, Viens, Frederi
core   +5 more sources

Structure–Function Decoupling of the Sensorimotor and Default Mode Networks in Black Americans With MS

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Objectives Multiple sclerosis (MS) exhibits racially disparate rates of disease progression. Black people with MS (B‐PwMS) experience a more severe disease course than non‐Hispanic White people with MS (NHW‐PwMS). Here we investigated structural and functional connectivity as well as structure–function decoupling in the ...
Emilio Cipriano, Giacomo Boffa, Maria Petracca, Marta Ponzano, Nicole Graziano, Claire Wigley, Claire Riley, Jonathan Howard, Pietro Bontempi, Sylvia Klineova, Fred Lublin, Matilde Inglese   +11 more
wiley   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Polynomial Chaos Expanded Gaussian Process

Machine Learning and Knowledge Extraction
In complex and unknown processes, global models are fitted over the entire input domain but often tend to perform poorly whenever the response surface exhibits non-stationary behavior and varying smoothness.
Dominik Polke, Tim Kösters, Elmar Ahle, Dirk Söffker   +3 more
doaj   +1 more source

Process‐Informed Analysis of As‐Built Metal Additive Surface Features

Advanced Engineering Materials, EarlyView.
This article introduces a novel method for feature‐based surface texture characterisation directly incorporating manufacturing variables into the feature extraction workflow. This marks a major step towards identifying process‐specific surface properties and their influence on part function and hence a holistic understanding of process–structure ...
Theresa Buchenau, Adam Thompson, Hauke Brüning, Marc Amkreutz, Bernd Mayer, Samanta Piano   +5 more
wiley   +1 more source