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Author: Tony Pourmohamad
Publisher: Springer Nature
ISBN: 3030824586
Category : Mathematics
Languages : en
Pages : 113
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Book Description
This book introduces readers to Bayesian optimization, highlighting advances in the field and showcasing its successful applications to computer experiments. R code is available as online supplementary material for most included examples, so that readers can better comprehend and reproduce methods. Compact and accessible, the volume is broken down into four chapters. Chapter 1 introduces the reader to the topic of computer experiments; it includes a variety of examples across many industries. Chapter 2 focuses on the task of surrogate model building and contains a mix of several different surrogate models that are used in the computer modeling and machine learning communities. Chapter 3 introduces the core concepts of Bayesian optimization and discusses unconstrained optimization. Chapter 4 moves on to constrained optimization, and showcases some of the most novel methods found in the field. This will be a useful companion to researchers and practitioners working with computer experiments and computer modeling. Additionally, readers with a background in machine learning but minimal background in computer experiments will find this book an interesting case study of the applicability of Bayesian optimization outside the realm of machine learning.
Author: Tony Pourmohamad
Publisher: Springer Nature
ISBN: 3030824586
Category : Mathematics
Languages : en
Pages : 113
Get Book Here
Book Description
This book introduces readers to Bayesian optimization, highlighting advances in the field and showcasing its successful applications to computer experiments. R code is available as online supplementary material for most included examples, so that readers can better comprehend and reproduce methods. Compact and accessible, the volume is broken down into four chapters. Chapter 1 introduces the reader to the topic of computer experiments; it includes a variety of examples across many industries. Chapter 2 focuses on the task of surrogate model building and contains a mix of several different surrogate models that are used in the computer modeling and machine learning communities. Chapter 3 introduces the core concepts of Bayesian optimization and discusses unconstrained optimization. Chapter 4 moves on to constrained optimization, and showcases some of the most novel methods found in the field. This will be a useful companion to researchers and practitioners working with computer experiments and computer modeling. Additionally, readers with a background in machine learning but minimal background in computer experiments will find this book an interesting case study of the applicability of Bayesian optimization outside the realm of machine learning.
Author: Daniel Packwood
Publisher: Springer
ISBN: 9811067813
Category : Technology & Engineering
Languages : en
Pages : 51
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Book Description
This book provides a short and concise introduction to Bayesian optimization specifically for experimental and computational materials scientists. After explaining the basic idea behind Bayesian optimization and some applications to materials science in Chapter 1, the mathematical theory of Bayesian optimization is outlined in Chapter 2. Finally, Chapter 3 discusses an application of Bayesian optimization to a complicated structure optimization problem in computational surface science.Bayesian optimization is a promising global optimization technique that originates in the field of machine learning and is starting to gain attention in materials science. For the purpose of materials design, Bayesian optimization can be used to predict new materials with novel properties without extensive screening of candidate materials. For the purpose of computational materials science, Bayesian optimization can be incorporated into first-principles calculations to perform efficient, global structure optimizations. While research in these directions has been reported in high-profile journals, until now there has been no textbook aimed specifically at materials scientists who wish to incorporate Bayesian optimization into their own research. This book will be accessible to researchers and students in materials science who have a basic background in calculus and linear algebra.
Author: David Sweet
Publisher: Simon and Schuster
ISBN: 1638356904
Category : Computers
Languages : en
Pages : 246
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Book Description
Optimize the performance of your systems with practical experiments used by engineers in the world’s most competitive industries. In Experimentation for Engineers: From A/B testing to Bayesian optimization you will learn how to: Design, run, and analyze an A/B test Break the "feedback loops" caused by periodic retraining of ML models Increase experimentation rate with multi-armed bandits Tune multiple parameters experimentally with Bayesian optimization Clearly define business metrics used for decision-making Identify and avoid the common pitfalls of experimentation Experimentation for Engineers: From A/B testing to Bayesian optimization is a toolbox of techniques for evaluating new features and fine-tuning parameters. You’ll start with a deep dive into methods like A/B testing, and then graduate to advanced techniques used to measure performance in industries such as finance and social media. Learn how to evaluate the changes you make to your system and ensure that your testing doesn’t undermine revenue or other business metrics. By the time you’re done, you’ll be able to seamlessly deploy experiments in production while avoiding common pitfalls. About the technology Does my software really work? Did my changes make things better or worse? Should I trade features for performance? Experimentation is the only way to answer questions like these. This unique book reveals sophisticated experimentation practices developed and proven in the world’s most competitive industries that will help you enhance machine learning systems, software applications, and quantitative trading solutions. About the book Experimentation for Engineers: From A/B testing to Bayesian optimization delivers a toolbox of processes for optimizing software systems. You’ll start by learning the limits of A/B testing, and then graduate to advanced experimentation strategies that take advantage of machine learning and probabilistic methods. The skills you’ll master in this practical guide will help you minimize the costs of experimentation and quickly reveal which approaches and features deliver the best business results. What's inside Design, run, and analyze an A/B test Break the “feedback loops” caused by periodic retraining of ML models Increase experimentation rate with multi-armed bandits Tune multiple parameters experimentally with Bayesian optimization About the reader For ML and software engineers looking to extract the most value from their systems. Examples in Python and NumPy. About the author David Sweet has worked as a quantitative trader at GETCO and a machine learning engineer at Instagram. He teaches in the AI and Data Science master's programs at Yeshiva University. Table of Contents 1 Optimizing systems by experiment 2 A/B testing: Evaluating a modification to your system 3 Multi-armed bandits: Maximizing business metrics while experimenting 4 Response surface methodology: Optimizing continuous parameters 5 Contextual bandits: Making targeted decisions 6 Bayesian optimization: Automating experimental optimization 7 Managing business metrics 8 Practical considerations
Author: Martin Pelikan
Publisher: Springer Science & Business Media
ISBN: 9783540237747
Category : Computers
Languages : en
Pages : 194
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Book Description
This book provides a framework for the design of competent optimization techniques by combining advanced evolutionary algorithms with state-of-the-art machine learning techniques. The book focuses on two algorithms that replace traditional variation operators of evolutionary algorithms by learning and sampling Bayesian networks: the Bayesian optimization algorithm (BOA) and the hierarchical BOA (hBOA). BOA and hBOA are theoretically and empirically shown to provide robust and scalable solution for broad classes of nearly decomposable and hierarchical problems. A theoretical model is developed that estimates the scalability and adequate parameter settings for BOA and hBOA. The performance of BOA and hBOA is analyzed on a number of artificial problems of bounded difficulty designed to test BOA and hBOA on the boundary of their design envelope. The algorithms are also extensively tested on two interesting classes of real-world problems: MAXSAT and Ising spin glasses with periodic boundary conditions in two and three dimensions. Experimental results validate the theoretical model and confirm that BOA and hBOA provide robust and scalable solution for nearly decomposable and hierarchical problems with only little problem-specific information.
Author: Jonas Mockus
Publisher: Springer Science & Business Media
ISBN: 9400909098
Category : Computers
Languages : en
Pages : 267
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Book Description
·Et moi ... si j'avait su comment en revcnir. One service mathematics has rendered the je o'y semis point alle.' human race. It has put common sense back Jules Verne where it beloogs. on the topmost shelf next to the dusty canister labelled 'discarded non The series is divergent; therefore we may be sense', able to do something with it. Eric T. BclI O. Heaviside Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and non linearities abound. Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sciences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One service topology has rendered mathematical physics ... '; 'One service logic has rendered com puter science .. .'; 'One service category theory has rendered mathematics .. .'. All arguably true. And all statements obtainable this way form part of the raison d'etre of this series.
Author: Roman Garnett
Publisher: Cambridge University Press
ISBN: 1108623557
Category : Computers
Languages : en
Pages : 376
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Book Description
Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond. This timely text provides a self-contained and comprehensive introduction to the subject, starting from scratch and carefully developing all the key ideas along the way. This bottom-up approach illuminates unifying themes in the design of Bayesian optimization algorithms and builds a solid theoretical foundation for approaching novel situations. The core of the book is divided into three main parts, covering theoretical and practical aspects of Gaussian process modeling, the Bayesian approach to sequential decision making, and the realization and computation of practical and effective optimization policies. Following this foundational material, the book provides an overview of theoretical convergence results, a survey of notable extensions, a comprehensive history of Bayesian optimization, and an extensive annotated bibliography of applications.
Author: John Winn
Publisher: CRC Press
ISBN: 1498756824
Category : Business & Economics
Languages : en
Pages : 469
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Book Description
Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system. The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem. Features: Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems. Explains machine learning concepts as they arise in real-world case studies. Shows how to diagnose, understand and address problems with machine learning systems. Full source code available, allowing models and results to be reproduced and explored. Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.
Author: Francesco Archetti
Publisher: Springer Nature
ISBN: 3030244946
Category : Business & Economics
Languages : en
Pages : 137
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Book Description
This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems. The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities.
Author: Anatoly Zhigljavsky
Publisher: Springer Nature
ISBN: 3030647129
Category : Mathematics
Languages : en
Pages : 125
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Book Description
Accessible to a variety of readers, this book is of interest to specialists, graduate students and researchers in mathematics, optimization, computer science, operations research, management science, engineering and other applied areas interested in solving optimization problems. Basic principles, potential and boundaries of applicability of stochastic global optimization techniques are examined in this book. A variety of issues that face specialists in global optimization are explored, such as multidimensional spaces which are frequently ignored by researchers. The importance of precise interpretation of the mathematical results in assessments of optimization methods is demonstrated through examples of convergence in probability of random search. Methodological issues concerning construction and applicability of stochastic global optimization methods are discussed, including the one-step optimal average improvement method based on a statistical model of the objective function. A significant portion of this book is devoted to an analysis of high-dimensional global optimization problems and the so-called ‘curse of dimensionality’. An examination of the three different classes of high-dimensional optimization problems, the geometry of high-dimensional balls and cubes, very slow convergence of global random search algorithms in large-dimensional problems , and poor uniformity of the uniformly distributed sequences of points are included in this book.
Author: Bianca M. Colosimo
Publisher: CRC Press
ISBN: 1420010700
Category : Business & Economics
Languages : en
Pages : 350
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Book Description
Although there are many Bayesian statistical books that focus on biostatistics and economics, there are few that address the problems faced by engineers. Bayesian Process Monitoring, Control and Optimization resolves this need, showing you how to oversee, adjust, and optimize industrial processes. Bridging the gap between application and dev
