Results 51 to 60 of about 168,901 (276)

SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics

Advanced Science, EarlyView.
ABSTRACT Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we propose SpatialESD, a Spatial
Hongyan Cao, Gaiqin Liu, Jingyi Xia, Runle Chen, Tong Wang, Xiaoling Yang, Ruiling Fang, Yanhong Luo, Ping Zeng, Hongmei Yu, Yanbo Zhang, Yuehua Cui   +11 more
wiley   +1 more source

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, 2021
" Logistic MCMC "
openaire   +1 more source

The Geography of Success: A Spatial Analysis of Export Intensity in the Italian Wine Industry

Agribusiness, EarlyView.
ABSTRACT This paper investigates the paradox of how Italy's fragmented, SME‐dominated wine industry achieves global export success. Moving beyond purely firm‐centric explanations, we test whether export intensity is spatially dependent, clustering geographically in regional ecosystems.
Nicolas Depetris Chauvin, Jonas Di Vita
wiley   +1 more source

Markov Chain Monte Carlo Based Energy Use Behaviors Prediction of Office Occupants

Algorithms, 2020
Prediction of energy use behaviors is a necessary prerequisite for designing personalized and scalable energy efficiency programs. The energy use behaviors of office occupants are different from those of residential occupants and have not yet been ...
Qiao Yan, Xiaoqian Liu, Xiaoping Deng, Wei Peng, Guiqing Zhang   +4 more
doaj   +1 more source

Bayesian Estimation of Mixed Multinomial Logit Models: Advances and Simulation-Based Evaluations

, 2019
Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of mixed multinomial logit (MMNL) models.
Bansal, Prateek, Bierlaire, Michel, Daziano, Ricardo A., Krueger, Rico, Rashidi, Taha H.   +4 more
core   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Orthogonal MCMC algorithms [PDF]

2014 IEEE Workshop on Statistical Signal Processing (SSP), 2014
Monte Carlo (MC) methods are widely used in signal processing, machine learning and stochastic optimization. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce a novel parallel interacting MCMC scheme, where the parallel chains share information using another MCMC technique working on the entire ...
Martino, Luca, Elvira Arregui, Víctor, Luengo García, David, Artés Rodríguez, Antonio, Corander, Jukka   +4 more
openaire   +3 more sources

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

Comparison of item response theory ability and item parameters according to classical and Bayesian estimation methods

International Journal of Assessment Tools in Education
This research aims to compare the ability and item parameter estimations of Item Response Theory according to Maximum likelihood and Bayesian approaches in different Monte Carlo simulation conditions.
Ergül Demir, Eray Selçuk
doaj   +1 more source

Nonasymptotic bounds on the mean square error for MCMC estimates via renewal techniques [PDF]

, 2010
The Nummellin’s split chain construction allows to decompose a Markov chain Monte Carlo (MCMC) trajectory into i.i.d. "excursions". Regenerative MCMC algorithms based on this technique use a random number of samples.
Miasojedow, Błażej, Niemiro, Wojciech, Łatuszyński, Krzysztof   +2 more
core   +3 more sources