Results 71 to 80 of about 810,691 (251)
Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm [PDF]
, 2013 We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data.
Cooke, Emma J. +17 more
core +1 more source
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 +4 more
wiley +1 more source
Computer Assisted Surgery, 2017 Background and objective: Image segmentation is a preliminary and fundamental step in computer aided magnetic resonance imaging (MRI) images analysis. But the performance of most current image segmentation methods is easily depreciated by noise in MRI ...
Mingsheng Chen +2 more
doaj +1 more source
Bayesian Clustering of Categorical Time Series Using Finite Mixtures of Markov Chain Models [PDF]
Two approaches for model-based clustering of categorical time series based on time- homogeneous first-order Markov chains are discussed. For Markov chain clustering the in- dividual transition probabilities are fixed to a group-specific transition matrix.
Sylvia Frühwirth-Schnatter +1 more
core
Clustering behaviour in Markov chains with eigenvalues close to one
, 2018 Finite, discrete, time-homogeneous Markov chains are frequently used as a simple mathematical model of real-world dynamical systems. In many such applications, an analysis of clustering behaviour in the states of the system is desirable, and it is well ...
Mahsa Faizrahnemoon +9 more
core +1 more source
Driver Behaviour Clustering Using Discrete PDFs and Modified Markov Algorithm
, 2021 NoThis paper presents a novel approach for probabilistic clustering, motivated by a real-world problem of modelling driving behaviour. The main aim is to establish clusters of drivers with similar journey behaviour, based on a large sample of historic ...
Amr Abdullatif +13 more
core +1 more source
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 +5 more
wiley +1 more source
Spectral Clustering for Non-reversible Markov Chains
, 2018 Spectral clustering methods are based on solving eigenvalue problems for the identification of clusters, e.g. the identification of metastable subsets of a Markov chain. Usually, real-valued eigenvectors are mandatory for this type of algorithms.
Weber, Marcus +2 more
core +3 more sources
A Bayesian Analysis of FemaleWage Dynamics Using Markov Chain Clustering
Austrian Journal of Statistics, 2016 In this work, we analyze wage careers of women in Austria. We identify groups of female employees with similar patterns in their earnings development. Covariates such as e.g.
Christoph Pamminger, Regina Tüchler
doaj +1 more source
A Bayesian Analysis of Female Wage Dynamics Using Markov Chain Clustering [PDF]
In this work, we analyze wage careers of women in Austria. We identify groups of female employees with similar patterns in their earnings development. Covariates such as e.g.
Christoph Pamminger, Regina Tüchler
core