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Benchmarking, Temporal Distribution, and Reconciliation Methods for Time Series
This book discusses the statistical methods most often applied for such adjustments, ranging from ad hoc procedures to regression-based models. The latter are emphasized, because of their clarity, ease of application, and superior results. Each topic is illustrated with many real case examples. In order to facilitate understanding of their properties and limitations of the methods discussed, a real data example, the Canada Total Retail Trade Series, is followed throughout the book.This book brings together the scattered literature on these topics and presents them using a consistent notation and a unifying view.
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 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.
Basics of Aerothermodynamics
The discrete numerical methods of aerodynamics/aerothermodynamics permit now - what was twenty years ago not imaginable - the simulation of high speed flows past real flight vehicle configurations with thermo-chemical and viscous effects, the description of the latter being still handicapped by in sufficient flow-physics models.
Basic Python for Data Management, Finance, and Marketing : Advance Your Career by Learning the Most Powerful Analytical Tool
Learn how to gather, manipulate, and analyze data with Python. This book is a practical guide to help you get started with Python from ground zero and to the point where you can use coding for everyday tasks. Python is used in all aspects of financial industry, from algo trading, reporting and risk management to building valuations models and predictive machine learning programs. You will: Get started with Python from square one / Extend what's possible on excel with Python / Automate tasks with Python / Analyze data more precisely
Basic Probability Theory with Applications
This book presents elementary probability theory with interesting and well-chosen applications that illustrate the theory. An introductory chapter reviews the basic elements of differential calculus which are used in the material to follow. The theory is presented systematically, beginning with the main results in elementary probability theory. This is followed by material on random variables. Random vectors, including the all important central limit theorem, are treated next. The last three chapters concentrate on applications of this theory in the areas of reliability theory, basic queuing models, and time series. Examples are elegantly woven into the text and over 400 exercises reinforce the material and provide students with ample practice.
Bacterial Genomes and Infectious Diseases
This book imparts fundamental knowledge on the structure, organization, and evolution of bacterial genomes. The value and power of comparative genomics and proteomics, bioinformatics, microarrays, and knockout animal models in analyzing genomes, bacteria-host interactions and disease are demonstrated. Also discussed are the genomes of virulent and nonvirulent strains and species, origin and evolution of pathogens, different models of bacteria-host interactions, and diseases mechanisms.
Bacterial Biofilms
This volume tends to focus on the biology of biofilms that affect human disease. It opens with chapters that provide the reader with current perspectives on biofilm development, physiology, environmental and regulatory effects, the role of quorum sensing, and resistance/phenotypic persistence to antimicrobial agents during biofilm growth. The next chapters are devoted to common problematic biofilms, those that colonize venous and urinary catheters. The final series of chapters examines biofilm formation by four species that are important pathogens and well studied models, one of which, Yersinia pestis, cleverly adopts a biofilm state of growth within its insect vector to promote disease transmission to mammalian hosts.
B2B eCommerce : Basics, Business Models and Best Practices in Business-to-Business Online Trade
Covers the basics of business-to-business (B2B) eCommerce, where similar principles of customer targeting can be observed as in B2C eCommerce. Gerrit Heinemann highlights the specifics and business models of B2B eCommerce, analyzes the digital challenges and shows the consequences and opportunities for online sales in B2B. Recognised best-practice examples illustrate how successful B2B eCommerce can work and which risks have to be considered.
Avatars at Work and Play : Collaboration and Interaction in Shared Virtual Environments
examining uses of shared virtual environments in practical settings such as scientific collaboration, distributed meetings, building models together, and others. It also covers online gaming in virtual environments, which has attracted hundreds of thousands of users and presents an opportunity for studying a myriad of social issues. Covering both ‘work’ and ‘play’, the volume brings together issues common to the two areas.
Autonomous Navigation in Dynamic Environments
The purpose of this book is to address the challenging problem of Autonomous Navigation in Dynamic Environments, and to present new ideas and approaches in this newly emerging technical domain. The book surveys the state-of-the-art, discusses in detail various related challenging technical aspects, and addresses upcoming technologies in this field. The aim of the book is to establish a foundation for a broad class of mobile robot mapping and navigation methodologies for indoor, outdoor, and exploratory missions.Three main topics located on the cutting edge of the state of the art are addressed, from both the theoretical and technological point of views: Dynamic world understanding and modelling for safe navigation, Obstacle avoidance and motion planning in dynamic environments, and Human-robot physical interactions. Several models and approaches are proposed for solving problems such as Simultaneous Localization and Mapping (SLAM) in dynamic environments, Mobile obstacle detection and tracking, World state estimation and motion prediction, Safe navigation in dynamic environments, Motion planning in dynamic environments, Robust decision making under uncertainty, and Human-Robot physical interactions.
Autonomous control for a reliable internet of services : Methods, models, approaches, techniques, algorithms, and tools
This open access book was prepared as a Final Publication of the COST Action IC1304 “Autonomous Control for a Reliable Internet of Services (ACROSS)”. The book contains 14 chapters and constitutes a show-case of the main outcome of the Action in line with its scientific goals. It will serve as a valuable reference for undergraduate and post-graduate students, educators, faculty members, researchers, engineers, and research strategists working in this field. The objective of this book is, by applying a systematic approach, to assess the state-of-the-art and consolidate the main research results achieved in this area.
Automotive Control Systems : For Engine, Driveline, and Vehicle
Reflecting the trend to optimization through integrative approaches for engine, driveline and vehicle control, this book enables control engineers to understand engine and vehicle models necessary for controller design and also introduces mechanical engineers to vehicle-specific signal processing and automatic control. The emphasis on measurement, comparisons between performance and modelling, and realistic examples derive from the authors’ industrial experience at Bosch and interactions within IFAC and SAE. The second edition offers new or expanded topics such as diesel-engine modelling, diagnosis and anti-jerking control, and vehicle modelling and parameter estimation. The book addresses professional engineers as well as students.
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.
Automated technology for verification and analysis ; 5th International Symposium, ATVA 2007 Tokyo, Japan, October 22-25, 2007 Proceedings
This book presented theoretical methods to achieve correct software or hardware systems, including both functional and non functional aspects
Automated technology for verification and analysis ; 4th International Symposium, ATVA 2006, Beijing, China, October 23-26, 2006, Proceedings
The Automated Technology for Veri?cation and Analysis (ATVA) international symposium series was initiated in 2003, The main topics of the symposium include th- ries useful for providing designers with automated support for obtaining correct software or hardware systems, as well as the implementation of such theories in tools or their application. In the end, 35 papers were selected for inclusion in the program. ATVA 2006 had three keynote speeches given respectively by Thomas Ball, Jin Yang, and Mihalis Yannakakis. The main symposium was preceded by a tutorial day, consisting of three two-hourlectures given by the keynotespeakers.
Auditory signal processing : Physiology, psychoacoustics, and models
The volume includes a total of 62 invited papers, organized into 12 broad thematic areas: cochlear signal processing; brainstem signal processing; pitch; frequency modulation; streaming; amplitude modulation; responses to complex sounds; speech; comodulation masking release; binaural hearing; temporal coding; and plasticity
