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| bayesian approach to global optimization: Bayesian Approach to Global Optimization Jonas Mockus, 2012-12-06 ·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. |
| bayesian approach to global optimization: The Bayesian Approach to Global Optimization Jonas Mockus, 1984 |
| bayesian approach to global optimization: Bayesian Approach to Global Optimization Jonas Mockus, 1989-02-28 |
| bayesian approach to global optimization: Bayesian Heuristic Approach to Discrete and Global Optimization Jonas Mockus, William Eddy, Gintaras Reklaitis, 2013-03-09 Bayesian decision theory is known to provide an effective framework for the practical solution of discrete and nonconvex optimization problems. This book is the first to demonstrate that this framework is also well suited for the exploitation of heuristic methods in the solution of such problems, especially those of large scale for which exact optimization approaches can be prohibitively costly. The book covers all aspects ranging from the formal presentation of the Bayesian Approach, to its extension to the Bayesian Heuristic Strategy, and its utilization within the informal, interactive Dynamic Visualization strategy. The developed framework is applied in forecasting, in neural network optimization, and in a large number of discrete and continuous optimization problems. Specific application areas which are discussed include scheduling and visualization problems in chemical engineering, manufacturing process control, and epidemiology. Computational results and comparisons with a broad range of test examples are presented. The software required for implementation of the Bayesian Heuristic Approach is included. Although some knowledge of mathematical statistics is necessary in order to fathom the theoretical aspects of the development, no specialized mathematical knowledge is required to understand the application of the approach or to utilize the software which is provided. Audience: The book is of interest to both researchers in operations research, systems engineering, and optimization methods, as well as applications specialists concerned with the solution of large scale discrete and/or nonconvex optimization problems in a broad range of engineering and technological fields. It may be used as supplementary material for graduate level courses. |
| bayesian approach to global optimization: A Fully Bayesian Approach to the Efficient Global Optimization Algorithm Sam D. Tajbakhsh, 2013 |
| bayesian approach to global optimization: Bayesian and High-Dimensional Global Optimization Anatoly Zhigljavsky, Antanas Žilinskas, 2021-03-02 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. |
| bayesian approach to global optimization: Bayesian Heuristic Approach to Discrete and Global Optimization Jonas Mockus, William Eddy, Gintaras Reklaitis, 1996-12-31 Bayesian decision theory is known to provide an effective framework for the practical solution of discrete and nonconvex optimization problems. This book is the first to demonstrate that this framework is also well suited for the exploitation of heuristic methods in the solution of such problems, especially those of large scale for which exact optimization approaches can be prohibitively costly. The book covers all aspects ranging from the formal presentation of the Bayesian Approach, to its extension to the Bayesian Heuristic Strategy, and its utilization within the informal, interactive Dynamic Visualization strategy. The developed framework is applied in forecasting, in neural network optimization, and in a large number of discrete and continuous optimization problems. Specific application areas which are discussed include scheduling and visualization problems in chemical engineering, manufacturing process control, and epidemiology. Computational results and comparisons with a broad range of test examples are presented. The software required for implementation of the Bayesian Heuristic Approach is included. Although some knowledge of mathematical statistics is necessary in order to fathom the theoretical aspects of the development, no specialized mathematical knowledge is required to understand the application of the approach or to utilize the software which is provided. Audience: The book is of interest to both researchers in operations research, systems engineering, and optimization methods, as well as applications specialists concerned with the solution of large scale discrete and/or nonconvex optimization problems in a broad range of engineering and technological fields. It may be used as supplementary material for graduate level courses. |
| bayesian approach to global optimization: Bayesian Data Analysis, Third Edition Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin, 2013-11-01 Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page. |
| bayesian approach to global optimization: Bayesian Heuristic Approach to Discrete and Global Optimization Jonas Mockus, William Eddy, Gintaras Reklaitis, 2014-01-15 |
| bayesian approach to global optimization: Process Optimization Enrique del Castillo, 2007-09-14 PROCESS OPTIMIZATION: A Statistical Approach is a textbook for a course in experimental optimization techniques for industrial production processes and other noisy systems where the main emphasis is process optimization. The book can also be used as a reference text by Industrial, Quality and Process Engineers and Applied Statisticians working in industry, in particular, in semiconductor/electronics manufacturing and in biotech manufacturing industries. |
| bayesian approach to global optimization: Encyclopedia of Optimization Christodoulos A. Floudas, Panos M. Pardalos, 2008-09-04 The goal of the Encyclopedia of Optimization is to introduce the reader to a complete set of topics that show the spectrum of research, the richness of ideas, and the breadth of applications that has come from this field. The second edition builds on the success of the former edition with more than 150 completely new entries, designed to ensure that the reference addresses recent areas where optimization theories and techniques have advanced. Particularly heavy attention resulted in health science and transportation, with entries such as Algorithms for Genomics, Optimization and Radiotherapy Treatment Design, and Crew Scheduling. |
| bayesian approach to global optimization: Handbook of Parallel Computing and Statistics Erricos John Kontoghiorghes, 2005-12-21 Technological improvements continue to push back the frontier of processor speed in modern computers. Unfortunately, the computational intensity demanded by modern research problems grows even faster. Parallel computing has emerged as the most successful bridge to this computational gap, and many popular solutions have emerged based on its concepts |
| bayesian approach to global optimization: Global Optimization Methods in Geophysical Inversion Mrinal K. Sen, Paul L. Stoffa, 2013-02-21 An up-to-date overview of global optimization methods used to formulate and interpret geophysical observations, for researchers, graduate students and professionals. |
| bayesian approach to global optimization: On a Bayesian Approach to Univariate Global Optimization Hansen, P. (Pierre), Jaumard, Brigitte, Lu, Shi-Hui, Groupe d'études et de recherche en analyse des décisions (Montréal, Québec), 1990 |
| bayesian approach to global optimization: Theory and Principled Methods for the Design of Metaheuristics Yossi Borenstein, Alberto Moraglio, 2013-12-19 Metaheuristics, and evolutionary algorithms in particular, are known to provide efficient, adaptable solutions for many real-world problems, but the often informal way in which they are defined and applied has led to misconceptions, and even successful applications are sometimes the outcome of trial and error. Ideally, theoretical studies should explain when and why metaheuristics work, but the challenge is huge: mathematical analysis requires significant effort even for simple scenarios and real-life problems are usually quite complex. In this book the editors establish a bridge between theory and practice, presenting principled methods that incorporate problem knowledge in evolutionary algorithms and other metaheuristics. The book consists of 11 chapters dealing with the following topics: theoretical results that show what is not possible, an assessment of unsuccessful lines of empirical research; methods for rigorously defining the appropriate scope of problems while acknowledging the compromise between the class of problems to which a search algorithm is applied and its overall expected performance; the top-down principled design of search algorithms, in particular showing that it is possible to design algorithms that are provably good for some rigorously defined classes; and, finally, principled practice, that is reasoned and systematic approaches to setting up experiments, metaheuristic adaptation to specific problems, and setting parameters. With contributions by some of the leading researchers in this domain, this book will be of significant value to scientists, practitioners, and graduate students in the areas of evolutionary computing, metaheuristics, and computational intelligence. |
| bayesian approach to global optimization: Data Science – Analytics and Applications Peter Haber, Thomas J. Lampoltshammer, Helmut Leopold, Manfred Mayr, 2022-03-29 Organizations have moved already from the rigid structure of classical project management towards the adoption of agile approaches. This holds also true for software development projects, which need to be flexible to adopt to rapid requests of clients as well to reflect changes that are required due to architectural design decisions. With data science having established itself as corner stone within organizations and businesses, it is now imperative to perform this crucial step for analytical business processes as well. The non-deterministic nature of data science and its inherent analytical tasks require an interactive approach towards an evolutionary step-by-step development to realize core essential business applications and use cases. The 4th International Data Science Conference (iDSC) 2021 brought together researchers, scientists, and business experts to discuss means of establishing new ways of embracing agile approaches within the various domains of data science, such as machine learning and AI, data mining, or visualization and communication as well as case studies and best practices from leading research institutions and business companies. The proceedings include all full papers presented in the scientific track and the corresponding German abstracts as well as the short papers from the student track. Among the topics of interest are: Artificial Intelligence and Machine Learning Implementation of data mining processes Agile Data Science and Visualization Case Studies and Applications for Agile Data Science --- Organisationen sind bereits von der starren Struktur des klassischen Projektmanagements zu agilen Ansätzen übergegangen. Dies gilt auch für Softwareentwicklungsprojekte, die flexibel sein müssen, um schnell auf die Wünsche der Kunden reagieren zu können und um Änderungen zu berücksichtigen, die aufgrund von Architekturentscheidungen erforderlich sind. Nachdem sich die Datenwissenschaft als Eckpfeiler in Organisationen und Unternehmen etabliert hat, ist es nun zwingend erforderlich, diesen entscheidenden Schritt auch für analytische Geschäftsprozesse durchzuführen. Die nicht-deterministische Natur der Datenwissenschaft und die ihr innewohnenden analytischen Aufgaben erfordern einen interaktiven Ansatz für eine evolutionäre, schrittweise Entwicklung zur Realisierung der wichtigsten Geschäftsanwendungen und Anwendungsfälle. Die 4. Internationale Konferenz zur Datenwissenschaft (iDSC 2021) brachte Forscher, Wissenschaftler und Wirtschaftsexperten zusammen, um Möglichkeiten zu erörtern, wie neue Wege zur Umsetzung agiler Ansätze in den verschiedenen Bereichen der Datenwissenschaft, wie maschinelles Lernen und KI, Data Mining oder Visualisierung und Kommunikation, sowie Fallstudien und Best Practices von führenden Forschungseinrichtungen und Wirtschaftsunternehmen etabliert werden können. Der Tagungsband umfasst alle im wissenschaftlichen Track vorgestellten Volltexte und die Kurzbeiträge aus dem studentischen Track auf Englisch und die dazugehörigen Abstracts auf Deutsch. Zu den Themen, die sie interessieren, gehören unter anderem: Künstliche Intelligenz und Maschinelles Lernen Implementierung von Data-Mining-Prozessen Agile Datenwissenschaft und Visualisierung Fallstudien und Anwendungen für Agile Datenwissenschaft |
| bayesian approach to global optimization: A Set of Examples of Global and Discrete Optimization Jonas Mockus, 2000-07-31 This book shows how to improve well-known heuristics by randomizing and optimizing their parameters. The ten in-depth examples are designed to teach operations research and the theory of games and markets using the Internet. Each example is a simple representation of some important family of real-life problems. Remote Internet users can run the accompanying software. The supporting web sites include software for Java, C++, and other languages. Audience: Researchers and specialists in operations research, systems engineering and optimization methods, as well as Internet applications experts in the fields of economics, industrial and applied mathematics, computer science, engineering, and environmental sciences. |
| bayesian approach to global optimization: Calibration of Watershed Models Qingyun Duan, Hoshin V. Gupta, Soroosh Sorooshian, Alain N. Rousseau, Richard Turcotte, 2003-01-10 Published by the American Geophysical Union as part of the Water Science and Application Series, Volume 6. During the past four decades, computer-based mathematical models of watershed hydrology have been widely used for a variety of applications including hydrologic forecasting, hydrologic design, and water resources management. These models are based on general mathematical descriptions of the watershed processes that transform natural forcing (e.g., rainfall over the landscape) into response (e.g., runoff in the rivers). The user of a watershed hydrology model must specify the model parameters before the model is able to properly simulate the watershed behavior. |
| bayesian approach to global optimization: Topics in Semidefinite and Interior-Point Methods Panos M. Pardalos and Henry Wolkowicz, 1998 Contains papers presented at a workshop held at The Fields Institute in May 1996. Papers are arranged in sections on theory, applications, and algorithms. Specific topics include testing the feasibility of semidefinite programs, semidefinite programming and graph equipartition, the totally nonnegative completion problem, approximation clustering, and cutting plane algorithms for semidefinite relaxations. For graduate students and researchers in mathematics, computer science, engineering, and operations. No index. Annotation copyrighted by Book News, Inc., Portland, OR |
| bayesian approach to global optimization: Handbook of Design and Analysis of Experiments Angela Dean, Max Morris, John Stufken, Derek Bingham, 2015-06-26 This carefully edited collection synthesizes the state of the art in the theory and applications of designed experiments and their analyses. It provides a detailed overview of the tools required for the optimal design of experiments and their analyses. The handbook covers many recent advances in the field, including designs for nonlinear models and algorithms applicable to a wide variety of design problems. It also explores the extensive use of experimental designs in marketing, the pharmaceutical industry, engineering and other areas. |
| bayesian approach to global optimization: Handbook of Stochastic Analysis and Applications D. Kannan, V. Lakshmikantham, 2001-10-23 An introduction to general theories of stochastic processes and modern martingale theory. The volume focuses on consistency, stability and contractivity under geometric invariance in numerical analysis, and discusses problems related to implementation, simulation, variable step size algorithms, and random number generation. |
| bayesian approach to global optimization: Large Scale Computations in Air Pollution Modelling Zahari Zlatev, Jørgen Brandt, Peter J.H. Builtjes, Gregory Carmichael, Ivan Dimov, Jack Dongarra, H. Van Dop, Krassimir Georgiev, Heinz Hass, Roberto San José, 2012-12-06 1. Contents of these proceedings. These proceedings contain most of the papers which were presented at the NATO ARW (Advanced Research Workshop) on Large Scale Computations in Air Pollution Modelling. The workshop was held, from June 6 to June to, 1998, in Residence Bistritza, a beautiful site near Sofia, the capital of Bulgaria, and at the foot of the mountain Vitosha. 2. Participants in the NATO ARW. Scientists from 23 countries in Europe, North America and Asia attended the meeting and participated actively in the discussions. The total number of participants was 57. The main topic of the discussions was the role of the large mathematical models in resolving difficult problems connected with the protection of our environment. 3. Major topics discussed at the workshop. The protection of our environment is one of the most important problems facing modern society. The importance of this problem has steadily increased during the last two-three decades, and environment protection will become even more important in the next century. Reliable and robust control strategies for keeping the pollution caused by harmful chemical compounds under certain safe levels have to be developed and used in a routine way. Large mathematical models, in which all important physical and chemical processes are adequately described, can successfully be used to solve this task. |
| bayesian approach to global optimization: Recent Developments in Mathematical Programming Santosh Kumar, 2022-01-26 This work is concerned with theoretical developments in the area of mathematical programming, development of new algorithms and software and their applications in science and industry. It aims to expose recent mathematical developments to a larger audience in science and industry. |
| bayesian approach to global optimization: Proceedings of the Fifth SIAM Conference on Parallel Processing for Scientific Computing J. J. Dongarra, 1992-01-01 This text gives the proceedings for the fifth conference on parallel processing for scientific computing. |
| bayesian approach to global optimization: Computational Science – ICCS 2022 Derek Groen, Clélia de Mulatier, Maciej Paszynski, Valeria V. Krzhizhanovskaya, Jack J. Dongarra, Peter M. A. Sloot, 2022-06-21 The four-volume set LNCS 13350, 13351, 13352, and 13353 constitutes the proceedings of the 22ndt International Conference on Computational Science, ICCS 2022, held in London, UK, in June 2022.* The total of 175 full papers and 78 short papers presented in this book set were carefully reviewed and selected from 474 submissions. 169 full and 36 short papers were accepted to the main track; 120 full and 42 short papers were accepted to the workshops/ thematic tracks. *The conference was held in a hybrid format Chapter “GPU Accelerated Modelling and Forecasting for Large Time Series” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. |
| bayesian approach to global optimization: Acta Numerica 2004: Volume 13 Arieh Iserles, 2004-06-03 An annual volume presenting substantive survey articles in numerical mathematics and scientific computing. |
| bayesian approach to global optimization: Handbook of Global Optimization Panos M. Pardalos, H. Edwin Romeijn, 2013-04-18 In 1995 the Handbook of Global Optimization (first volume), edited by R. Horst, and P.M. Pardalos, was published. This second volume of the Handbook of Global Optimization is comprised of chapters dealing with modern approaches to global optimization, including different types of heuristics. Topics covered in the handbook include various metaheuristics, such as simulated annealing, genetic algorithms, neural networks, taboo search, shake-and-bake methods, and deformation methods. In addition, the book contains chapters on new exact stochastic and deterministic approaches to continuous and mixed-integer global optimization, such as stochastic adaptive search, two-phase methods, branch-and-bound methods with new relaxation and branching strategies, algorithms based on local optimization, and dynamical search. Finally, the book contains chapters on experimental analysis of algorithms and software, test problems, and applications. |
| bayesian approach to global optimization: Stochastic Programming Horand Gassmann, W. T. Ziemba, 2013 This book shows the breadth and depth of stochastic programming applications. All the papers presented here involve optimization over the scenarios that represent possible future outcomes of the uncertainty problems. The applications, which were presented at the 12th International Conference on Stochastic Programming held in Halifax, Nova Scotia in August 2010, span the rich field of uses of these models. The finance papers discuss such diverse problems as longevity risk management of individual investors, personal financial planning, intertemporal surplus management, asset management with benchmarks, dynamic portfolio management, fixed income immunization and racetrack betting. The production and logistics papers discuss natural gas infrastructure design, farming Atlantic salmon, prevention of nuclear smuggling and sawmill planning. The energy papers involve electricity production planning, hydroelectric reservoir operations and power generation planning for liquid natural gas plants. Finally, two telecommunication papers discuss mobile network design and frequency assignment problems. |
| bayesian approach to global optimization: Randomization Methods in Algorithm Design Panos M. Pardalos, Sanguthevar Rajasekaran, 1999 This volume is based on proceedings held during the DIMACS workshop on Randomization Methods in Algorithm Design in December 1997 at Princeton. The workshop was part of the DIMACS Special Year on Discrete Probability. It served as an interdisciplinary research workshop that brought together a mix of leading theorists, algorithmists and practitioners working in the theory and implementation aspects of algorithms involving randomization. Randomization has played an important role in the design of both sequential and parallel algorithms. The last decade has witnessed tremendous growth in the area of randomized algorithms. During this period, randomized algorithms went from being a tool in computational number theory to finding widespread applications in many problem domains. Major topics covered include randomization techniques for linear and integer programming problems, randomization in the design of approximate algorithms for combinatorial problems, randomization in parallel and distributed algorithms, practical implementation of randomized algorithms, de-randomization issues, and pseudo-random generators. This volume focuses on theory and implementation aspects of algorithms involving randomization. It would be suitable as a graduate or advanced graduate text. |
| bayesian approach to global optimization: Towards Global Optimisation Laurence Charles Ward Dixon, G. P. Szegö, 1975 |
| bayesian approach to global optimization: Experimentation for Engineers David Sweet, 2023-03-07 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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. 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 |
| bayesian approach to global optimization: PRIMA 2020: Principles and Practice of Multi-Agent Systems Takahiro Uchiya, Quan Bai, Iván Marsá Maestre, 2021-02-13 This book constitutes the refereed proceedings of the 23rd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2020, held in Nagoya, Japan, in November 2020. The 19 full papers presented and 13 short papers were carefully reviewed and selected from 50 submissions. Due to COVID-19, the conference was held online. The conference covers a wide range of ranging from foundations of agent theory and engineering aspects of agent systems, to emerging interdisciplinary areas of agent-based research. |
| bayesian approach to global optimization: Bayesian Cognitive Modeling Michael D. Lee, Eric-Jan Wagenmakers, 2014-04-03 Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions. |
| bayesian approach to global optimization: Web Information Systems and Applications Cheqing Jin, Shiyu Yang, Xuequn Shang, Haofen Wang, Yong Zhang, 2024-09-16 This book constitutes the refereed proceedings of the 21st International Conference on Web Information Systems and Applications, WISA 2024, held in Yinchuan, China, during August 2–4, 2024. The 39 full papers and 11 short papers presented in this book were carefully selected and reviewed from 193 submissions. These papers have been organized in the following topical sections: Knowledge construction; Intelligent service; Intelligent computing; Large language model; Security; Information system applications. |
| bayesian approach to global optimization: Introduction to Global Optimization R. Horst, Panos M. Pardalos, Nguyen Van Thoai, 1995-06-30 Global optimization concerns the computation and characterization of global optima of nonlinear functions. Such problems are widespread in the mathematical modelling of real systems in a very wide range of applications and the last 30 years have seen the development of many new theoretical, algorithmic and computational contributions which have helped to solve globally multiextreme problems in important practical applications. Most of the existing books on optimization focus on the problem of computing locally optimal solutions. Introduction to Global Optimization, however, is a comprehensive textbook on constrained global optimization that covers the fundamentals of the subject, presenting much new material, including algorithms, applications and complexity results for quadratic programming, concave minimization, DC and Lipschitz problems, and nonlinear network flow. Each chapter contains illustrative examples and ends with carefully selected exercises, designed to help students grasp the material and enhance their knowledge of the methods involved. Audience: Students of mathematical programming, and all scientists, from whatever discipline, who need global optimization methods in such diverse areas as economic modelling, fixed charges, finance, networks and transportation, databases, chip design, image processing, nuclear and mechanical design, chemical engineering design and control, molecular biology, and environmental engineering. |
| bayesian approach to global optimization: Simulation and Modeling Methodologies, Technologies and Applications Gerd Wagner, Frank Werner, Tuncer Oren, Floriano De Rango, 2023-02-10 The present book includes a set of selected papers from the 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2021) that was held as an online event, from July 7 to 9, 2021. The conference brought together researchers and practitioners interested in methodologies and applications of modeling and simulation. New and innovative solutions are reported in this book. A selection was made after the conference, based also on the conference chairs assessment, reviewers’ assessment, quality of presentation and audience interest, so that this book includes the extended and revised versions of the very best papers of the conference. |
| bayesian approach to global optimization: Designing Deep Learning Systems Chi Wang, Donald Szeto, 2023-07-25 To be practically usable, a deep learning model must be built into a software platform. As a software engineer, you need a deep understanding of deep learning to create such a system. This book gives you that depth. Designing deep learning systems: a guide for software engineers teaches you everything you need to design and implement a production-ready deep learning platform. First, it presents the big picture of a deep learning system from the developer's perspective, including its majot components and how they are connected. Then, it carefully guides you through the engineering methods you'll need to build your own maintainable, efficient, and scalable deep learning platforms. |
| bayesian approach to global optimization: Average-Case Analysis of Numerical Problems Klaus Ritter, 2007-05-06 The average-case analysis of numerical problems is the counterpart of the more traditional worst-case approach. The analysis of average error and cost leads to new insight on numerical problems as well as to new algorithms. The book provides a survey of results that were mainly obtained during the last 10 years and also contains new results. The problems under consideration include approximation/optimal recovery and numerical integration of univariate and multivariate functions as well as zero-finding and global optimization. Background material, e.g. on reproducing kernel Hilbert spaces and random fields, is provided. |
| bayesian approach to global optimization: Recent Advances in Parallel Virtual Machine and Message Passing Interface Jack Dongarra, Peter Kacsuk, Norbert Podhorszki, 2003-06-26 Parallel Virtual Machine (PVM) and Message Passing Interface (MPI) are the most frequently used tools for programming according to the message passing paradigm, which is considered one of the best ways to develop parallel applications. This volume comprises 42 revised contributions presented at the Seventh European PVM/MPI Users’ Group Meeting, which was held in Balatonfr ed, Hungary, 10 13 September 2000. The conference was organized by the Laboratory of Parallel and Distributed Systems of the Computer and Automation Research Institute of the Hungarian Academy of Sciences. This conference was previously held in Barcelona, Spain (1999), Liverpool, UK (1998) and Cracow, Poland (1997). The first three conferences were devoted to PVM and were held at the Technische Universit t M nchen, Germany (1996), Ecole Normale Superieure Lyon, France (1995), and University of Rome, Italy (1994). This conference has become a forum for users and developers of PVM, MPI, and other message passing environments. Interaction between those groups has proved to be very useful for developing new ideas in parallel computing and for applying existing ideas to new practical fields. The main topics of the meeting were evaluation and performance of PVM and MPI, extensions and improvements to PVM and MPI, algorithms using the message passing paradigm, and applications in science and engineering based on message passing. The conference included four tutorials and five invited talks on advances in MPI, cluster computing, network computing, grid computing, and SGI parallel computers and programming systems. |
| bayesian approach to global optimization: Artificial Intelligence in Manufacturing Masoud Soroush, Richard D Braatz, 2024-01-22 Artificial Intelligence in Manufacturing: Applications and Case Studies provides detailed technical descriptions of emerging applications of AI in manufacturing using case studies to explain implementation. Artificial intelligence is increasingly being applied to all engineering disciplines, producing insights into how we understand the world and allowing us to create products in new ways. This book unlocks the advantages of this technology for manufacturing by drawing on work by leading researchers who have successfully used it in a range of applications. Processes including additive manufacturing, pharmaceutical manufacturing, painting, chemical engineering and machinery maintenance are all addressed. Case studies, worked examples, basic introductory material and step-by-step instructions on methods make the work accessible to a large group of interested professionals. - Explains innovative computational tools and methods in a practical and systematic way - Addresses a wide range of manufacturing types, including additive, chemical and pharmaceutical - Includes case studies from industry that describe how to overcome the challenges of implementing these methods in practice |
What exactly is a Bayesian model? - Cross Validated
Dec 14, 2014 · Bayesian Analysis, 1(1):1-40. there are 2 answers: Your model is first Bayesian if it uses Bayes' rule (that's the "algorithm"). More broadly, if you infer (hidden) causes from a …
Posterior Predictive Distributions in Bayesian Statistics - Physics …
Feb 17, 2021 · Confessions of a moderate Bayesian, part 4. Bayesian statistics by and for non-statisticians. Read part 1: How to Get Started with Bayesian Statistics. Read part 2: …
When are Bayesian methods preferable to Frequentist?
Jun 17, 2014 · The Bayesian, on the other hand, think that we start with some assumption about the parameters (even if unknowingly) and use the data to refine our opinion about those …
Bayesian vs frequentist Interpretations of Probability
Bayesian probability frames problems in e.g. statistics in quite a different way, which the other answers discuss. The Bayesian system seems to be a direct application of the theory of …
How to choose prior in Bayesian parameter estimation
Dec 15, 2014 · The problem is that if you choose non-conjugate priors, you cannot make exact Bayesian inference (simply put, you cannot derive a close-form posterior). Rather, you need to …
bayesian - What is an "uninformative prior"? Can we ever have …
In an interesting twist, some researchers outside the Bayesian perspective have been developing procedures called confidence distributions that are probability distributions on the parameter …
Help me understand Bayesian prior and posterior distributions
See also this reference for a short but imho good overview of Bayesian reasoning and simple analysis. A longer introduction for conjugate analyses, especially for binomial data can be …
Should Bayesian inference be avoided with a small sample size and ...
Jul 19, 2023 · With small n and no reliable prior, instead of a Bayesian analysis---or even a Frequentist analysis (which may just confirm that "The sample is too small to estimate these …
What is the best introductory Bayesian statistics textbook?
My bayesian-guru professor from Carnegie Mellon agrees with me on this. having the minimum knowledge of statistics and R and Bugs(as the easy way to DO something with Bayesian stat) …
What are the cons of Bayesian analysis? - Cross Validated
Oct 18, 2011 · I am a Bayesian by inclination, but generally a frequentist in practice. The reason for this is usually that performing the full Bayesian analysis properly (rather than e.g. MAP …
What exactly is a Bayesian model? - Cross Validated
Dec 14, 2014 · Bayesian Analysis, 1(1):1-40. there are 2 answers: Your model is first Bayesian if it uses Bayes' rule (that's the "algorithm"). More broadly, if you infer (hidden) causes from a …
Posterior Predictive Distributions in Bayesian Statistics - Physics …
Feb 17, 2021 · Confessions of a moderate Bayesian, part 4. Bayesian statistics by and for non-statisticians. Read part 1: How to Get Started with Bayesian Statistics. Read part 2: …
When are Bayesian methods preferable to Frequentist?
Jun 17, 2014 · The Bayesian, on the other hand, think that we start with some assumption about the parameters (even if unknowingly) and use the data to refine our opinion about those …
Bayesian vs frequentist Interpretations of Probability
Bayesian probability frames problems in e.g. statistics in quite a different way, which the other answers discuss. The Bayesian system seems to be a direct application of the theory of …
How to choose prior in Bayesian parameter estimation
Dec 15, 2014 · The problem is that if you choose non-conjugate priors, you cannot make exact Bayesian inference (simply put, you cannot derive a close-form posterior). Rather, you need to …
bayesian - What is an "uninformative prior"? Can we ever have …
In an interesting twist, some researchers outside the Bayesian perspective have been developing procedures called confidence distributions that are probability distributions on the parameter …
Help me understand Bayesian prior and posterior distributions
See also this reference for a short but imho good overview of Bayesian reasoning and simple analysis. A longer introduction for conjugate analyses, especially for binomial data can be …
Should Bayesian inference be avoided with a small sample size and ...
Jul 19, 2023 · With small n and no reliable prior, instead of a Bayesian analysis---or even a Frequentist analysis (which may just confirm that "The sample is too small to estimate these …
What is the best introductory Bayesian statistics textbook?
My bayesian-guru professor from Carnegie Mellon agrees with me on this. having the minimum knowledge of statistics and R and Bugs(as the easy way to DO something with Bayesian stat) …
What are the cons of Bayesian analysis? - Cross Validated
Oct 18, 2011 · I am a Bayesian by inclination, but generally a frequentist in practice. The reason for this is usually that performing the full Bayesian analysis properly (rather than e.g. MAP …
