Results 131 to 140 of about 12,746,553 (278)

An Unscented Kalman Filter Based on the Adams-Bashforth Method with Applications to the State Estimation of Osprey-Type Drones Composed of Tiltable Rotor Mechanisms. [PDF]

Sensors (Basel)
Watanabe K, Takeda S, Nagai I.
europepmc   +1 more source

Sequential Monte Carlo with likelihood tempering and parallel implementation for uncertainty quantification

AIChE Journal, EarlyView.
Abstract Bayesian estimation enables uncertainty quantification, but analytical implementation is often intractable. As an approximate approach, the Markov Chain Monte Carlo (MCMC) method is widely used, though it entails a high computational cost due to frequent evaluations of the likelihood function.
Tatsuki Maruchi, Tomoyuki Yajima, Yoshiaki Kawajiri   +2 more
wiley   +1 more source

A Functional Joint Model for Survival and Multivariate Sparse Functional Data in Multi-Cohort Alzheimer's Disease Study. [PDF]

Stat Med
Wang W, Xiao L, Li R, Luo S, Alzheimer's Disease Neuroimaging Initiative.   +4 more
europepmc   +1 more source

Domain‐Aware Implicit Network for Arbitrary‐Scale Remote Sensing Image Super‐Resolution

Advanced Intelligent Discovery, EarlyView.
Although existing arbitrary‐scale image super‐resolution methods are flexible to reconstruct images with arbitrary scales, the characteristic of training distribution is neglected that there exists domain shift between samples of various scales. In this work, a Domain‐Aware Implicit Network (DAIN) is proposed to handle it from the perspective of domain
Xiaoxuan Ren, Qian Jiang, Xin Jin, Puming Wang, Xing Chu, Shaowen Yao, Shengfa Miao   +6 more
wiley   +1 more source

Dynamic Factor Analysis for Sparse and Irregular Longitudinal Data: An Application to Metabolite Measurements in a COVID-19 Study. [PDF]

Stat Med
Cai J, Goudie RJB, Tom BDM.
europepmc   +1 more source

Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review

Advanced Intelligent Discovery, EarlyView.
Feature selection combined with machine learning and high‐throughput experimentation enables efficient handling of high‐dimensional datasets in emerging photovoltaics. This approach accelerates material discovery, improves process optimization, and strengthens stability prediction, while overcoming challenges in data quality and model scalability to ...
Jiyun Zhang, Jiayi Tan, Qizhen Song, Tian Du, Jens Hauch, Christoph J. Brabec   +5 more
wiley   +1 more source

Neuroimaging PheWAS and molecular phenotyping implicate PSMC3 in Alzheimer's disease. [PDF]

Alzheimers Dement
Bledsoe X, Wang TC, Wu Y, Archer D, Chen HH, Naj AC, Bush WS, Hohman TJ, Dumitrescu L, Below JE, Gamazon ER.   +10 more
europepmc   +1 more source

A covariance function for feed intake, live weight, and milk yield estimated using a random regression model.

Journal of Dairy Science, 1999
R. Veerkamp, Robin Thompson
semanticscholar   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Posterior estimation of longitudinal variance components from nonlongitudinal data using Bayesian Gaussian process model. [PDF]

Genetics
Arjas A, Leppälä K, Sillanpää MJ.
europepmc   +1 more source