Results 31 to 40 of about 4,387 (252)
Monotone bivariate Markov kernels with specified marginals [PDF]
Proceedings of the American Mathematical Society, 2010 Summary: Given two Markov kernels \( k\) and \( k'\) on an ordered Polish space, such that \( k\) is stochastically dominated by \( k'\), we establish the existence of: (i) a monotone bivariate Markov kernel whose marginals are \( k\) and \( k'\) and (ii) an upward coupler from \( k\) to \( k'\). This extends the results of \textit{V.
Machida, Motoya, Shibakov, Alexander
openaire +1 more source
Refined n-Valued Neutrosophic Markov Decision Processes for Quality Evaluation of Talent Cultivation in Vocational Education under Emerging Productive Forces [PDF]
Neutrosophic Sets and SystemsEmerging Productive Forces (EPF) including artificial intelligence, green manufacturing, and digital platforms change skill requirements faster than classical curriculum planning can adapt.
Wei Zhao
doaj +1 more source
The Hypergroup Property and Representation of Markov Kernels [PDF]
, 2008 accept\'e au "S\'eminaire de Probabilit\'es"
Bakry, Dominique, Huet, Nolwen
openaire +3 more sources
KVLMM: A Trajectory Prediction Method Based on a Variable-Order Markov Model With Kernel Smoothing
IEEE Access, 2018 With the dramatic proliferation of global positioning system (GPS) devices, a rich range of research has been conducted on the analysis of GPS trajectories.
Xing Wang +3 more
doaj +1 more source
LIBRJMCMC: AN OPEN-SOURCE GENERIC C++ LIBRARY FOR STOCHASTIC OPTIMIZATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 The librjmcmc is an open source C++ library that solves optimization problems using a stochastic framework. The library is primarily intended for but not limited to research purposes in computer vision, photogrammetry and remote sensing, as it
M. Brédif, O. Tournaire, O. Tournaire
doaj +1 more source
Neural Networks and Markov Categories
AppliedMathWe present a formal framework for modeling neural network dynamics using Category Theory, specifically through Markov categories. In this setting, neural states are represented as objects and state transitions as Markov kernels, i.e., morphisms in the ...
Sebastian Pardo-Guerra +3 more
doaj +1 more source
GINI: from ISH images to gene interaction networks. [PDF]
PLoS Computational Biology, 2013 Accurate inference of molecular and functional interactions among genes, especially in multicellular organisms such as Drosophila, often requires statistical analysis of correlations not only between the magnitudes of gene expressions, but also between ...
Kriti Puniyani, Eric P Xing
doaj +1 more source
Scientific Reports, 2023 The evolvement in digital media and information technology over the past decades have purveyed the internet to be an effectual medium for the exchange of data and communication.
Deepa D. Shankar +1 more
doaj +1 more source
Markov Kernels Local Aggregation for Noise Vanishing Distribution Sampling
SIAM Journal on Mathematics of Data Science, 2022 A novel strategy that combines a given collection of $\pi$-reversible Markov kernels is proposed. At each Markov transition, one of the available kernels is selected via a state-dependent probability distribution. In contrast to random-scan type approaches that assume a constant (i.e.
Maire, Florian, Vandekerkhove, Pierre
openaire +4 more sources
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 +11 more
wiley +1 more source