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Author: Chandra Shekhar
Publisher: CRC Press
ISBN: 1040031471
Category : Technology & Engineering
Languages : en
Pages : 249
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Book Description
In an era dominated by mathematical and statistical models, this book unravels the profound significance of these tools in decoding uncertainties within numerical, observational, and calculation-based data. From governmental institutions to private entities, statistical prediction models provide a critical framework for optimal decision-making, offering nuanced insights into diverse realms, from climate to production and beyond. This book ·Serves as a comprehensive resource in statistical modeling, methodologies, and optimization techniques across various domains. ·Features contributions from global authors; the compilation comprises 10 insightful chapters, each addressing critical aspects of estimation and optimization through statistical modeling. ·Covers a spectrum of topics, from non-parametric goodness-of-fit statistics to Bayesian applications; the book explores novel resampling methods, advanced measures for empirical mode, and transient behavior analysis in queueing systems. ·Includes asymptotic properties of goodness-of-fit statistics, practical applications of Bayesian Statistics, modifications to the Hard EM algorithm, and explicit transient probabilities. ·Culminates with an exploration of an inventory model for perishable items, integrating preservation technology and learning effects to determine the economic order quantity. This book stands as a testament to global collaboration, offering a rich tapestry of commendable statistical and mathematical modeling alongside real-world problem-solving. It is poised to ignite further exploration, discussion, and innovation in the realms of statistical modeling and optimization.
Author: Chandra Shekhar
Publisher: CRC Press
ISBN: 1040031471
Category : Technology & Engineering
Languages : en
Pages : 249
Get Book Here
Book Description
In an era dominated by mathematical and statistical models, this book unravels the profound significance of these tools in decoding uncertainties within numerical, observational, and calculation-based data. From governmental institutions to private entities, statistical prediction models provide a critical framework for optimal decision-making, offering nuanced insights into diverse realms, from climate to production and beyond. This book ·Serves as a comprehensive resource in statistical modeling, methodologies, and optimization techniques across various domains. ·Features contributions from global authors; the compilation comprises 10 insightful chapters, each addressing critical aspects of estimation and optimization through statistical modeling. ·Covers a spectrum of topics, from non-parametric goodness-of-fit statistics to Bayesian applications; the book explores novel resampling methods, advanced measures for empirical mode, and transient behavior analysis in queueing systems. ·Includes asymptotic properties of goodness-of-fit statistics, practical applications of Bayesian Statistics, modifications to the Hard EM algorithm, and explicit transient probabilities. ·Culminates with an exploration of an inventory model for perishable items, integrating preservation technology and learning effects to determine the economic order quantity. This book stands as a testament to global collaboration, offering a rich tapestry of commendable statistical and mathematical modeling alongside real-world problem-solving. It is poised to ignite further exploration, discussion, and innovation in the realms of statistical modeling and optimization.
Author: Snezhana Gocheva-Ilieva
Publisher: Mdpi AG
ISBN: 9783036526928
Category : Mathematics
Languages : en
Pages : 184
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Book Description
The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section "Mathematics and Computer Science". Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties.
Author: Dirk P. Kroese
Publisher: Springer Science & Business Media
ISBN: 1461487757
Category : Computers
Languages : en
Pages : 412
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Book Description
This textbook on statistical modeling and statistical inference will assist advanced undergraduate and graduate students. Statistical Modeling and Computation provides a unique introduction to modern Statistics from both classical and Bayesian perspectives. It also offers an integrated treatment of Mathematical Statistics and modern statistical computation, emphasizing statistical modeling, computational techniques, and applications. Each of the three parts will cover topics essential to university courses. Part I covers the fundamentals of probability theory. In Part II, the authors introduce a wide variety of classical models that include, among others, linear regression and ANOVA models. In Part III, the authors address the statistical analysis and computation of various advanced models, such as generalized linear, state-space and Gaussian models. Particular attention is paid to fast Monte Carlo techniques for Bayesian inference on these models. Throughout the book the authors include a large number of illustrative examples and solved problems. The book also features a section with solutions, an appendix that serves as a MATLAB primer, and a mathematical supplement.
Author: Daniel P. Palomar
Publisher: Cambridge University Press
ISBN: 0521762227
Category : Computers
Languages : en
Pages : 513
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Book Description
Leading experts provide the theoretical underpinnings of the subject plus tutorials on a wide range of applications, from automatic code generation to robust broadband beamforming. Emphasis on cutting-edge research and formulating problems in convex form make this an ideal textbook for advanced graduate courses and a useful self-study guide.
Author: Jacob Elliot Mattingley
Publisher: Stanford University
ISBN:
Category :
Languages : en
Pages : 123
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Book Description
Convex optimization is widely used, in many fields, but is nearly always constrained to problems solved in a few minutes or seconds, and even then, nearly always with a human in the loop. The advent of parser-solvers has made convex optimization simpler and more accessible, and greatly increased the number of people using convex optimization. Most current applications, however, are for the design of systems or analysis of data. It is possible to use convex optimization for real-time or embedded applications, where the optimization solver is a part of a larger system. Here, the optimization algorithm must find solutions much faster than a generic solver, and often has a hard, real-time deadline. Use in embedded applications additionally means that the solver cannot fail, and must be robust even in the presence of relatively poor quality data. For ease of embedding, the solver should be simple, and have minimal dependencies on external libraries. Convex optimization has been successfully applied in such settings in the past. However, they have usually necessitated a custom, hand-written solver. This requires signficant time and expertise, and has been a major factor preventing the adoption of convex optimization in embedded applications. This work describes the implementation and use of a prototype code generator for convex optimization, CVXGEN, that creates high-speed solvers automatically. Using the principles of disciplined convex programming, CVXGEN allows the user to describe an optimization problem in a convenient, high-level language, then receive code for compilation into an extremely fast, robust, embeddable solver.
Author: Joseph K. Blitzstein
Publisher: CRC Press
ISBN: 1466575573
Category : Mathematics
Languages : en
Pages : 599
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Book Description
Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.
Author:
Publisher: UM Libraries
ISBN:
Category : Education, Higher
Languages : en
Pages : 1400
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Book Description
Author: University of Michigan. College of Engineering
Publisher: UM Libraries
ISBN:
Category : Engineering schools
Languages : en
Pages : 878
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Book Description
Author: University of Michigan
Publisher:
ISBN:
Category :
Languages : en
Pages : 1356
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Book Description
Announcements for the following year included in some vols.
Author: University of Michigan
Publisher:
ISBN:
Category : Detroit (Mich.)
Languages : en
Pages : 1318
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Book Description
Announcements for the following year included in some vols.
