Results 101 to 110 of about 776,491 (337)
Kernel-based diffusion approximated Markov decision processes for autonomous navigation and control on unstructured terrains [PDF]
, 2021 Junhong Xu +5 more
openalex +1 more source
Mathematical Methods in the Applied Sciences, EarlyView.We develop a full randomization of the classical hyper‐logistic growth model by obtaining closed‐form expressions for relevant quantities of interest, such as the first probability density function of its solution, the time until a given fixed population is reached, and the population at the inflection point.
Juan Carlos Cortés +2 more
wiley +1 more source
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
doaj +1 more source
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
On the Mean‐Field Limit of Consensus‐Based Methods
Mathematical Methods in the Applied Sciences, EarlyView.ABSTRACT Consensus‐based optimization (CBO) employs a swarm of particles evolving as a system of stochastic differential equations (SDEs). Recently, it has been adapted to yield a derivative free sampling method referred to as consensus‐based sampling (CBS). In this paper, we investigate the “mean‐field limit” of a class of consensus methods, including
Marvin Koß, Simon Weissmann, Jakob Zech
wiley +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
doaj +1 more source
International Journal of Mechanical System Dynamics, EarlyView.ABSTRACT Modern engineering systems require advanced uncertainty‐aware model updating methods that address parameter correlations beyond conventional interval analysis. This paper proposes a novel framework integrating Riemannian manifold theory with Gaussian Process Regression (GPR) for systems governed by Symmetric Positive‐Definite (SPD) matrix ...
Yanhe Tao +3 more
wiley +1 more source
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
doaj +1 more source
Detecting extirpation: A localized approach to a global problem
PLANTS, PEOPLE, PLANET, EarlyView.The global biodiversity crisis stems from a cascading series of extirpations driving species toward extinction. Addressing this crisis requires methods for early detection of extinction at local scales, where communities can mobilize conservation efforts.
Andrew D. F. Simon +4 more
wiley +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
doaj +1 more source