000			01677nam a22001937a 4500
008			200611b2018 ||||| |||| 00| 0 eng d
020			_a9781108472470
082			_a519.5 HAR-J
100			_aHarlim, John
245			_aData-driven computational methods : _bparameter and operator estimations / _cJohn Harlim
260			_aUnited Kingdom _bCambridge University Press _c2018
300			_a158 p.
365			_aGBP _b49.99.
500			_aModern scientific computational methods are undergoing a transformative change; big data and statistical learning methods now have the potential to outperform the classical first-principles modeling paradigm. This book bridges this transition, connecting the theory of probability, stochastic processes, functional analysis, numerical analysis, and differential geometry. It describes two classes of computational methods to leverage data for modeling dynamical systems. The first is concerned with data fitting algorithms to estimate parameters in parametric models that are postulated on the basis of physical or dynamical laws. The second is on operator estimation, which uses the data to nonparametrically approximate the operator generated by the transition function of the underlying dynamical systems. This self-contained book is suitable for graduate studies in applied mathematics, statistics, and engineering. Carefully chosen elementary examples with supplementary MATLAB® codes and appendices covering the relevant prerequisite materials are provided, making it suitable for self-study.
650			_aMathematical statistics
650			_aStochastic analysis
650			_aComputer science
650			_aStochastic models
999			_c65610 _d65610