الصفحة 37
الصفحة 37
Beginning Excel What-If Data Analysis tools : Getting started with goal seek, data tables, scenarios, and solver
Excels what-if data analysis tools let you experiment with your data to project future results. In turn, these predictions will lead to better decision making and unlock the mystery of many business analysis scenarios. For example, what-if data analysis tools will enable you to forecast how lowering the price per unitwhile increasing projected unit salesmight affect your profit margins. Beginning Excel What-If Data Analysis Tools explores the use of Goal Seek, Data Tables, Scenarios, and Solver to help you get insight on your data. This book is focused and to the point, and it provides tutorial treatment of what-if tools in a practical, hands-on manner.
Beginning deep learning with TensorFlow : Work with Keras, MNIST data sets, and advanced neural networks
Stats with an introduction to AI, where you’ll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, you’ll jump into simple classification programs for hand-writing analysis. Once you’ve tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, you’ll get into the heavy lifting of programming neural networks and working with a wide variety of neural network types such as GANs and RNNs. Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer! You will: Develop using deep learning algorithms Build deep learning models using TensorFlow 2 Create classification systems and other, practical deep learning applications
Bayesian reliability
Bayesian Reliability presents modern methods and techniques for analyzing reliability data from a Bayesian perspective. The adoption and application of Bayesian methods in virtually all branches of science and engineering have significantly increased over the past few decades. This increase is largely due to advances in simulation-based computational tools for implementing Bayesian methods. The authors extensively use such tools throughout this book, focusing on assessing the reliability of components and systems with particular attention to hierarchical models and models incorporating explanatory variables. Such models include failure time regression models, accelerated testing models, and degradation models. The authors pay special attention to Bayesian goodness-of-fit testing, model validation, reliability test design, and assurance test planning. Throughout the book, the authors use Markov chain Monte Carlo (MCMC) algorithms for implementing Bayesian analyses--algorithms that make the Bayesian approach to reliability computationally feasible and conceptually straightforward.
Bayesian networks and Influence diagrams : A guide to construction and analysis
Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty.
Bayesian Networks and Decision Graphs
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams.It contians two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems.
Bayesian Methods in the Search for MH370
This book demonstrates how nonlinear/non-Gaussian Bayesian time series estimation methods were used to produce a probability distribution of potential MH370 flight paths. It provides details of how the probabilistic models of aircraft flight dynamics, satellite communication system measurements, environmental effects and radar data were constructed and calibrated. The probability distribution was used to define the search zone in the southern Indian Ocean. The book describes particle-filter based numerical calculation of the aircraft flight-path probability distribution and validates the method using data from several of the involved aircraft’s previous flights. Finally it is shown how the Reunion Island flaperon debris find affects the search probability distribution.
Bayesian core : A practical approach to computational Bayesian statistics
This Bayesian modeling book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models.
Bayesian computation with R : Introduces Bayesian modeling by use of computation using the R language
R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language.
Basic Real Analysis
Basic Real Analysis and Advanced Real Analysis (available separately or together as a Set) systematically develop those concepts and tools in real analysis that are vital to every mathematician, whether pure or applied, aspiring or established. These works present a comprehensive treatment with a global view of the subject, emphasizing the connections between real analysis and other branches of mathematics.
B 2007 : Formal Specification and Development in B ; 7th International Conference of B Users, Besancon, France, January 7-19, 2007, Proceedings
These proceedingsrecordthe papers presented at the Seventh InternationalC- ference of B Users (B 2007), held in the city of Besan¸ con in the east of France. All the submitted papers in these proceedings were peer reviewed by at least three reviewers drawn from the B committee, depending on the subject matter of the paper. The authorsof the papersforB 2007werefrom Australia,Canada, Finland, Germany, France, Switzerland, and the UK. The conference featured a rangeof contributions by distinguished invited speakers drawn from both ind- try and academia.
Azaheterocycles Based on -, ß-Unsaturated Carbonyls
Devoted to heterocyclizations of aliphatic and aromatic, -unsaturated carbonyls with various binucleophiles leading to three-, five-, six and seven-membered partially hydrogenated nitrogen-containing heterocycles. During the last decade interest in these classes of organic c- pounds has been experiencing a scientific renaissance owing to their significant role in biological processes in living cells and diverse effects on physiological activities. In addition, such compounds are also more prevalent from the vi- point of ''classical'' problems of organic chemistry, among them reactivity, chemo- and regioselectivity, tautomerism, conformational analysis and features of their electronic structure. The character of these problems in the case of partially hydrogenated heterocycles differs sufficiently from that for hetero- omatized and perhydrogenated heterocyclic compounds and investigations in this field very often lead to interesting and unusual results. Extensively characterized cyclocondensations of, -unsaturated carbonyls, their synthetic equivalents and their precursors are the most widespread, facile and generally valid pathway to dihydroazaheterocycles.
Axiom of Choice
AC, the axiom of choice, because of its non-constructive character, is the most controversial mathematical axiom, shunned by some, used indiscriminately by others. This treatise shows paradigmatically that:Disasters happen without AC: Many fundamental mathematical results fail (being equivalent in ZF to AC or to some weak form of AC).Disasters happen with AC: Many undesirable mathematical monsters are being created (e.g., non measurable sets and undeterminate games).Illuminating examples are drawn from diverse areas of mathematics, particularly from general topology, but also from algebra, order theory, elementary analysis, measure theory, game theory, and graph theory.
Axial flux permanent magnet brushless machines
This book deals with the analysis, construction, design, control and applications of AFPM machines. The authors present their own research results, as well as significant research contributions made by others.
Axial flux permanent magnet brushless machines
Axial Flux Permanent Magnet (AFPM) brushless machines are modern electrical machines with a lot of advantageous merits over their conventional counterparts. They are increasingly used in power generation, domestic appliances, industrial drives, electric vehicles, and marine propulsion drives and many other applications. This book deals with the analysis, construction, design, optimisation, control and applications of AFPM machines. The authors present their own research results, as well as significant research contributions made by others. This monograph will be of interest to electrical engineers and other engineers involved in the design and application of AFPM brushless machine drives. It will be an important resource for researchers and graduate students in the field of electrical machine and drives.
Autonomy oriented computing : From problem solving to complex systems modeling
Autonomy Oriented Computing explores the important theoretical and practical issues in AOC, by analyzing methodologies and presenting experimental case studies. The book serves as a comprehensive reference source for researchers, scientists, engineers, and professionals in all fields concerned with this promising new development in computer science. It can also be used as a main or supplementary text in graduate and undergraduate programs across a broad range of computer-related disciplines, including Robotics and Automation, Amorphous Computing, Image Processing and Computer Vision, Programming Paradigms, Computational Biology, and many others. The first part of the book, Fundamentals, describes the basic concepts and characteristics of an AOC system, and then it enumerates the critical design and engineering issues faced in AOC system development. The second part of the book, AOC in Depth, provides a detailed analysis of methodologies and case studies to evaluate the use of AOC in problem solving and complex system modeling. The final chapter reviews the essential features of the AOC paradigm and outlines a number of possibilities for future research and development.
Autonomic and Trusted Computing ; 5th International Conference, ATC 2008, Oslo, Norway, June 23-25, 2008 Proceedings
This book constitutes the refereed procedings of the 5th International Conference on Autonomic and Trusted Computing, ATC 2008, held in Oslo, Norway, in June 2008, co-located with UIC 2008, the 5th International Conference on Ubiquitous Intelligence and Computing.
Automotive software - connected services in mobile networks ; 1st Automotive software workshop, ASWSD 2004, San Diego, CA, USA, January 10-12, 2004, Revised Selected Papers
Software development for the automotive domain is currently subject to a silent revolution. On the one hand, software has become the enabling technology for almost all safety-critical and comfort functions o?ered to the customer. A total of 90 % of all innovations in automotive systems are directly or indirectly - abled by software. the following list highlights three of the corresponding challenges: First, the dependencies between safety-critical and comfort functions are rapidly increasing;a simple example is the interplay of airbag controland power seat control in the case of an accident. Careful analysis and design of these dependencies are necessary to yield correct software solutions. Second, advances in wired and wireless networking infrastructures enable - terconnection between cars and backend service providers .
Automating business modelling : A guide to using logic to represent Informal methods and support reasoning
Enterprise Modelling (EM) methods are frequently used by entrepreneurs as an analysis tool for describing and redesigning their businesses. The resulting product, an enterprise model, is commonly used as a blueprint for reconstructing organizations and such effort is often a part of business process re-engineering and improvement initiatives. Automating Business Modelling describes different techniques of providing automated support for enterprise modelling methods and introduces universally used approaches. A running example of a business modelling method is included; providing a framework and detailed explanation as to how to construct automated support for modelling, allowing readers to follow the method to create similar support. Suitable for senior undergraduates and postgraduates of Business Studies, Computer Science and Artificial Intelligence, practitioners in the fields of Knowledge Management, Enterprise Modelling and Software Engineering, this book offers insight and know-how to both student and professional.
Automatic Differentiation : Applications, Theory, and Implementations
This collection covers the state of the art in automatic differentiation theory and practice. Practitioners and students will learn about advances in automatic differentiation techniques and strategies for the implementation of robust and powerful tools. Computational scientists and engineers will benefit from the discussion of applications, which provide insight into effective strategies for using automatic differentiation for design optimization, sensitivity analysis, and uncertainty quantification.
Automatic Autocorrelation and Spectral Analysis
It takes advantage of greater computing power and robust algorithms to produce enough candidate models to be sure of providing a suitable candidate for given data. Improved order selection quality guarantees that one of the best (and often the best) will be selected automatically. The data themselves suggest their best representation. Should the analyst wish to intervene, alternatives can be provided. Written for graduate signal processing students and for researchers and engineers using time series analysis for practical applications ranging from breakdown prevention in heavy machinery to measuring lung noise for medical diagnosis.
