Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications PDF Download

Author: Jürgen Pilz
Publisher: Springer Nature
ISBN: 3031400550
Category : Mathematics
Languages : en
Pages : 265

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Book Description
This volume presents a selection of articles on statistical modeling and simulation, with a focus on different aspects of statistical estimation and testing problems, the design of experiments, reliability and queueing theory, inventory analysis, and the interplay between statistical inference, machine learning methods and related applications. The refereed contributions originate from the 10th International Workshop on Simulation and Statistics, SimStat 2019, which was held in Salzburg, Austria, September 2–6, 2019, and were either presented at the conference or developed afterwards, relating closely to the topics of the workshop. The book is intended for statisticians and Ph.D. students who seek current developments and applications in the field.

Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
ISBN: 1107057132
Category : Computers
Languages : en
Pages : 415

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Book Description
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Author:
Publisher:
ISBN:
Category : Medicine
Languages : en
Pages : 742

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Book Description

Author: Srikanta Mishra
Publisher: Elsevier
ISBN: 0128032804
Category : Science
Languages : en
Pages : 252

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Book Description
Applied Statistical Modeling and Data Analytics: A Practical Guide for the Petroleum Geosciences provides a practical guide to many of the classical and modern statistical techniques that have become established for oil and gas professionals in recent years. It serves as a "how to" reference volume for the practicing petroleum engineer or geoscientist interested in applying statistical methods in formation evaluation, reservoir characterization, reservoir modeling and management, and uncertainty quantification. Beginning with a foundational discussion of exploratory data analysis, probability distributions and linear regression modeling, the book focuses on fundamentals and practical examples of such key topics as multivariate analysis, uncertainty quantification, data-driven modeling, and experimental design and response surface analysis. Data sets from the petroleum geosciences are extensively used to demonstrate the applicability of these techniques. The book will also be useful for professionals dealing with subsurface flow problems in hydrogeology, geologic carbon sequestration, and nuclear waste disposal. - Authored by internationally renowned experts in developing and applying statistical methods for oil & gas and other subsurface problem domains - Written by practitioners for practitioners - Presents an easy to follow narrative which progresses from simple concepts to more challenging ones - Includes online resources with software applications and practical examples for the most relevant and popular statistical methods, using data sets from the petroleum geosciences - Addresses the theory and practice of statistical modeling and data analytics from the perspective of petroleum geoscience applications

Author: Robert E. Kass
Publisher: Springer
ISBN: 1461496020
Category : Medical
Languages : en
Pages : 663

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Book Description
Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.

Author: Hui Chen
Publisher: Frontiers Media SA
ISBN: 2832537146
Category : Science
Languages : en
Pages : 108

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Book Description
Emerging from electromagnetic waves and fast extending to acoustic and elastic waves, metamaterials that exhibit extraordinary wave control abilities have been gaining soaring attention. Over the past two decades, elastic metamaterials with engineered microstructures have provided a variety of appealing solutions for controlling elastic waves and vibrations. By tailoring their internal microstructures at a subwavelength scale, elastic metamaterials fruitfully distinct themselves from traditional materials or phononic crystals by their striking functions in wave trajectory manipulation, cloaking, nonreciprocal and topological wave control, as well as low-frequency wave/vibration mitigation and absorption.

Author: Marc Kéry
Publisher: Elsevier
ISBN: 0443137161
Category : Science
Languages : en
Pages : 551

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Book Description
Applied Statistical Modelling for Ecologists provides a gentle introduction to the essential models of applied statistics: linear models, generalized linear models, mixed and hierarchical models. All models are fit with both a likelihood and a Bayesian approach, using several powerful software packages widely used in research publications: JAGS, NIMBLE, Stan, and TMB. In addition, the foundational method of maximum likelihood is explained in a manner that ecologists can really understand. This book is the successor of the widely used Introduction to WinBUGS for Ecologists (Kéry, Academic Press, 2010). Like its parent, it is extremely effective for both classroom use and self-study, allowing students and researchers alike to quickly learn, understand, and carry out a very wide range of statistical modelling tasks. The examples in Applied Statistical Modelling for Ecologists come from ecology and the environmental sciences, but the underlying statistical models are very widely used by scientists across many disciplines. This book will be useful for anybody who needs to learn and quickly become proficient in statistical modelling, with either a likelihood or a Bayesian focus, and in the model-fitting engines covered, including the three latest packages NIMBLE, Stan, and TMB. - Contains a concise and gentle introduction to probability and applied statistics as needed in ecology and the environmental sciences - Covers the foundations of modern applied statistical modelling - Gives a comprehensive, applied introduction to what currently are the most widely used and most exciting, cutting-edge model fitting software packages: JAGS, NIMBLE, Stan, and TMB - Provides a highly accessible applied introduction to the two dominant methods of fitting parametric statistical models: maximum likelihood and Bayesian posterior inference - Details the principles of model building, model checking and model selection - Adopts a "Rosetta Stone" approach, wherein understanding of one software, and of its associated language, will be greatly enhanced by seeing the analogous code in other engines - Provides all code available for download for students, at https://www.elsevier.com/books-and-journals/book-companion/9780443137150

Author: Averill Law
Publisher: McGraw-Hill Science/Engineering/Math
ISBN: 9780073294414
Category : Technology & Engineering
Languages : en
Pages : 792

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Book Description
Since the publication of the first edition in 1982, the goal of Simulation Modeling and Analysis has always been to provide a comprehensive, state-of-the-art, and technically correct treatment of all important aspects of a simulation study. The book strives to make this material understandable by the use of intuition and numerous figures, examples, and problems. It is equally well suited for use in university courses, simulation practice, and self study. The book is widely regarded as the “bible” of simulation and now has more than 100,000 copies in print. The book can serve as the primary text for a variety of courses; for example: • A first course in simulation at the junior, senior, or beginning-graduate-student level in engineering, manufacturing, business, or computer science (Chaps. 1 through 4, and parts of Chaps. 5 through 9). At the end of such a course, the students will be prepared to carry out complete and effective simulation studies, and to take advanced simulation courses. • A second course in simulation for graduate students in any of the above disciplines (most of Chaps. 5 through 12). After completing this course, the student should be familiar with the more advanced methodological issues involved in a simulation study, and should be prepared to understand and conduct simulation research. • An introduction to simulation as part of a general course in operations research or management science (part of Chaps. 1, 3, 5, 6, and 9).

Author: Robert B. Gramacy
Publisher: CRC Press
ISBN: 1000766527
Category : Mathematics
Languages : en
Pages : 677

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Book Description
Computer simulation experiments are essential to modern scientific discovery, whether that be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are meta-models of computer simulations, used to solve mathematical models that are too intricate to be worked by hand. Gaussian process (GP) regression is a supremely flexible tool for the analysis of computer simulation experiments. This book presents an applied introduction to GP regression for modelling and optimization of computer simulation experiments. Features: • Emphasis on methods, applications, and reproducibility. • R code is integrated throughout for application of the methods. • Includes more than 200 full colour figures. • Includes many exercises to supplement understanding, with separate solutions available from the author. • Supported by a website with full code available to reproduce all methods and examples. The book is primarily designed as a textbook for postgraduate students studying GP regression from mathematics, statistics, computer science, and engineering. Given the breadth of examples, it could also be used by researchers from these fields, as well as from economics, life science, social science, etc.

Author: Michael Erbschloe
Publisher: CRC Press
ISBN: 1351683322
Category : Computers
Languages : en
Pages : 260

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Book Description
There is extensive government research on cyber security science, technology, and applications. Much of this research will be transferred to the private sector to aid in product development and the improvement of protective measures against cyber warfare attacks. This research is not widely publicized. There are initiatives to coordinate these research efforts but there has never been a published comprehensive analysis of the content and direction of the numerous research programs. This book provides private sector developers, investors, and security planners with insight into the direction of the U.S. Government research efforts on cybersecurity.