Results 41 to 50 of about 51,780 (181)
An integral operator inequality with applications
Linear integral operators are defined acting in the Lebesgue integration spaces on intervals of the real line. A necessary and sufficient condition is given for these operators to be bounded, and a characterisation is given for the operator bounds ...
Everitt WN, Littlejohn LL, Chisholm RS
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ABSTRACT Data‐based learning of system dynamics allows model‐based control approaches to be applied to systems with partially unknown dynamics. Gaussian process regression is a preferred approach that outputs not only the learned system model but also the variance of the model, which can be seen as a measure of uncertainty.
Daniel Landgraf +2 more
wiley +1 more source
AGT: Efficient Offline Reinforcement Learning With Advantage‐Guided Transformer
ABSTRACT Offline reinforcement learning (RL) is a paradigm that seeks to train policies directly based on fixed datasets derived from previous interactions with the environment. However, offline RL faces critical challenges in environments characterised by sparse rewards and datasets dominated by suboptimal trajectories.
Jiaye Wei +4 more
wiley +1 more source
Residual Life Assessment of Oil‐Immersed Insulating Paper by FTIR Feature Interpretability Evolution
ABSTRACT The ageing of insulating paper escalates the peril of insulation failure in oil‐impregnated‐paper power equipment. Consequently, the real‐time monitoring and non‐destructive assessment of insulating paper condition assume paramount significance.
Guangyi Liu +8 more
wiley +1 more source
Measure Theory in Noncommutative Spaces
The integral in noncommutative geometry (NCG) involves a non-standard trace called a Dixmier trace. The geometric origins of this integral are well known. From a measure-theoretic view, however, the formulation contains several difficulties.
Steven Lord, Fedor Sukochev
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On Spatial Point Processes With Composition‐Valued Marks
Summary Methods for marked spatial point processes with scalar marks have seen extensive development in recent years. While the impressive progress in data collection and storage capacities has yielded an immense increase in spatial point process data with highly challenging non‐scalar marks, methods for their analysis are not equally well developed ...
Matthias Eckardt +2 more
wiley +1 more source
Change Point Analysis for Functional Data Using Empirical Characteristic Functionals
ABSTRACT We develop a new method to detect change points in the distribution of functional data based on integrated CUSUM processes of empirical characteristic functionals. Asymptotic results are presented under conditions allowing for low‐order moments and serial dependence in the data establishing the limiting null‐distribution of the proposed test ...
Lajos Horváth +2 more
wiley +1 more source
A New Approach to Statistical Inference for Functional Time Series
ABSTRACT The analysis of time‐indexed functional data plays an important role in the field of business and economic statistics. In the literature, statistical inference for functional time series often involves reducing the dimension of functional data to a finite dimension K$$ K $$, followed by the use of tools from multivariate analysis.
Hanjia Gao, Yi Zhang, Xiaofeng Shao
wiley +1 more source
Measure‐valued processes for energy markets
Abstract We introduce a framework that allows to employ (non‐negative) measure‐valued processes for energy market modeling, in particular for electricity and gas futures. Interpreting the process' spatial structure as time to maturity, we show how the Heath–Jarrow–Morton approach can be translated to this framework, thus guaranteeing arbitrage free ...
Christa Cuchiero +3 more
wiley +1 more source
Hilbert space methods for partial differential equations
This book is an outgrowth of a course which we have given almost periodically over the last eight years. It is addressed to beginning graduate students of mathematics, engineering, and the physical sciences.
Ralph E. Showalter
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