Results 91 to 100 of about 830,323 (220)
IEEE Access, 2018 Research on the uncertainty of wind power has a significant influence on power system planning and decision-making. This paper proposes a novel method for wind power interval forecasting based on rough sets theory, weighted Markov chain, and kernel ...
Xiyun Yang +3 more
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$V$-geometrical ergodicity of Markov kernels via finite-rank approximations
Electronic Communications in Probability, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hervé, Loïc, Ledoux, James
openaire +5 more sources
Non-Parametric Signal Interpolation
Austrian Journal of Statistics, 2016 This paper considers the problem of interpolation (smoothing) of a partially observable Markov random sequence. For the dynamic observation models, an equation for the interpolation of the posterior probability density is derived.
Alexandr V. Dobrovidov
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A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-Resolution [PDF]
Computer Vision and Pattern RecognitionDeep learning-based methods have achieved significant successes on solving the blind super-resolution (BSR) problem. However, most of them request supervised pretraining on labelled datasets.
Zhixiong Yang +7 more
semanticscholar +1 more source
MCMC based modelling of queuing systems from empirical data
Lietuvos Matematikos Rinkinys, 2011 Markov chain Monte Carlo (MCMC) modelling technique requires one to be able to construct a proposal density. There is no universal way to achieve this. This paper considers the universal proposal selection technique based on the kernel density estimate ...
Mantas Landauskas +1 more
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Modeling Temporal Structure in Music for Emotion Prediction using Pairwise Comparisons [PDF]
, 2014 The temporal structure of music is essential for the cognitive processes related to the emotions expressed in music. However, such temporal information is often disregarded in typical Music Information Retrieval modeling tasks of predicting higher-level ...
Larsen, Jens +2 more
core +1 more source
Self-Adversarially Learned Bayesian Sampling
, 2018 Scalable Bayesian sampling is playing an important role in modern machine learning, especially in the fast-developed unsupervised-(deep)-learning models.
Chen, Changyou +2 more
core +1 more source
A Probablistic Origin for a New Class of Bivariate Polynomials
Symmetry, Integrability and Geometry: Methods and Applications, 2008 We present here a probabilistic approach to the generation of new polynomials in two discrete variables. This extends our earlier work on the 'classical' orthogonal polynomials in a previously unexplored direction, resulting in the discovery of an ...
Michael R. Hoare, Mizan Rahman
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Asymptotics of a discrete-time particle system near a reflecting boundary
, 2012 We examine a discrete-time Markovian particle system on the quarter-plane introduced by M. Defosseux. The vertical boundary acts as a reflecting wall. The particle system lies in the Anisotropic Kardar-Parisi-Zhang with a wall universality class.
A. Aptekarev +12 more
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Multiple Kernel SVM Based on Two-Stage Learning
IEEE Access, 2020 In this paper we introduce the idea of two-stage learning for multiple kernel SVM (MKSVM) and present a new MKSVM algorithm based on two-stage learning (MKSVM-TSL). The first stage is the pre-learning and its aim is to obtain the information of data such
Xingrui Gong +5 more
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