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Data-driven Models in Inverse Problems
Advances in learning-based methods are revolutionizing several fields in applied mathematics, including inverse problems, resulting in a major paradigm shift towards data-driven …
Econometric Modelling and Analysis to Support Operational Policing
Applied Stochastic System Modeling
This book was written for an introductory one-semester or two-quarter course in stochastic processes and their applications. The reader is assumed to have a basic knowledge of …
Model-Based Design of Adaptive Embedded Systems
This book describes model-based development of adaptive embedded systems, which enable improved functionality using the same resources. The techniques presented facilitate design …
Longitudinal Structural Equation Modeling
This valuable book is now in a fully updated second edition that presents the latest developments in longitudinal structural equation modeling (SEM) and new chapters on missing …
Introduction to Mathematical Modeling of Crop Growth
Nistir 7784: Modeling and Simulation of Healthcare Systems for Homeland Security Applications
Design of Experiments for Generalized Linear Models
Generalized Linear Models (GLMs) allow many statistical analyses to be extended to important statistical distributions other than the Normal distribution. While numerous books …
Model-Driven Software Systems Development Using the Meta-Artifact Process
The importance of architecture for software systems is widely accepted, but the role of architecture in the overall development process is not so clear. Presenting an …
Simulating Copulas: Stochastic Models, Sampling Algorithms, And Applications
'The book remains a valuable tool both for statisticians who are already familiar with the theory of copulas and just need to develop sampling algorithms, and for practitioners who …
Numerical Models in Geomechanics
In this volume a number of developments on a variety of topics have been reported. These topics include: partially saturated soil; instabilities in soil behaviour; environmental …
Stochastic Models for Prices Dynamics in Energy and Commodity Markets
This monograph presents a theory for random field models in time and space, viewed as stochastic processes with values in a Hilbert space, to model the stochastic dynamics of …