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Perturbations, Optimization, and Statistics

A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical;

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Introduction to Optimization Methods and their Application in Statistics

to optimization methods and their use in several important areas of statistics. It does not pretend to provide either a complete treatment;

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Optimization

Lange is a Springer author of other successful books. This is the first book that emphasizes the applications of optimization to statistics;

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Maths and Stats for Web Analytics and Conversion Optimization

Maths and Stats for Web Analytics and Conversion Optimization is een boek van Himanshu Sharma uitgegeven bij Blurb. ISBN 9781364849184

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Computationally Intensive Statistics for Intelligent IoT

systems, statistical techniques, linear algebra and its role in optimization techniques, evolution of optimization techniques, optimal utilization;

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Statistics for Engine Optimization

challenge with increased confidence. Statistics for Engine Optimization offers the knowledge and experience of the engineers and the statisticians;

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Open Problems in Optimization and Data Analysis

Computational and theoretical open problems in optimization, computational geometry, data science, logistics, statistics, supply chain;

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Stochastic Modeling and Optimization

This books covers the broad range of research in stochastic models and optimization. Applications presented include networks, financial;

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Modeling, Computation And Optimization

of advertising, application of reliability models in economics, support vector machines, optimization, complementarity modeling and games.;

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Design and Analysis of Sensory Optimization

. The heart of this book is product optimization which covers in great detail designs and analysis of optimization studies with;

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Riemannian Optimization and Its Applications

optimization problems, discusses the theoretical properties of these algorithms, and suggests possible applications of Riemannian optimization to;

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Optimization Heuristics In Econometrics

Global and combinatorial optimization heuristics are widely used in different areas ranging from engineering to operational research. The;

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Statistical Inference via Convex Optimization

optimization theory can be used to devise and analyze near-optimal statistical inferences. Statistical Inference via Convex Optimization is an;

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Statistics, Data Analysis And Decision Modeling

control; risk analysis and Monte-Carlo simulation; systems simulation modeling and analysis; selection models and decision analysis; optimization;

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Data Science for Business and Decision Making

methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics;

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Support Vector Machines

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components;

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Advanced Calculus with Applications in Statistics

features detailed coverage of optimization techniques and their applications in statistics while introducing the reader to approximation theory;

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Quadratic Programming with Computer Programs

Optimization and structural plasticity. This is useful in Civil Engineering as well as Statistics.;

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Quadratic Programming with Computer Programs

Optimization and structural plasticity. This is useful in Civil Engineering as well as Statistics.;

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Handbook of Semidefinite Programming

Semidefinite programming (SDP) is one of the most exciting and active research areas in optimization. It has and continues to attract;

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Handbook of Semidefinite Programming

Semidefinite programming (SDP) is one of the most exciting and active research areas in optimization. It has and continues to attract;

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Decentralized Control of Complex Systems

Complex systems require fast control action in response to local input, and perturbations dictate the use of decentralized information and;

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Python for MATLAB Development

of computational problems in science, engineering, optimization, statistics, finance, and simulation.;

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Optimization Models

the field of optimization, focusing on powerful and reliable convex optimization techniques. Students and practitioners will learn how to;

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Algorithms for Optimization

optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic;

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Convex Optimization

Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and;

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Norm Estimations for Operator Valued Functions and Their Applications

Intended for specialists in functional analysis and stability theory, this work presents a systematic exposition of estimations for norms;

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