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This revised textbook motivates and illustrates the techniques of applied probability by applications in electrical engineering and computer science (EECS). The author presents information processing and communication systems that use algorithms based on probabilistic models and techniques, including web searches, digital links, speech recognition, GPS, route planning, recommendation systems, classification, and estimation. He then explains how these applications work and, along the way, provides the readers with the understanding of the key concepts and methods of applied probability. Python labs enable the readers to experiment and consolidate their understanding. The book includes homework, solutions, and Jupyter notebooks. This edition includes new topics such as Boosting, Multi-armed bandits, statistical tests, social networks, queuing networks, and neural networks. For ancillaries related to this book, including examples of Python demos and also Python labs used in Berkeley, please email Mary James at mary.james@springer.com. This is an open access book.

Maths for computer scientists --- Communications engineering / telecommunications --- Maths for engineers --- Probability & statistics --- Probability and Statistics in Computer Science --- Communications Engineering, Networks --- Mathematical and Computational Engineering --- Probability Theory and Stochastic Processes --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences --- Mathematical and Computational Engineering Applications --- Probability Theory --- Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences --- Applied probability --- Hypothesis testing --- Detection theory --- Expectation maximization --- Stochastic dynamic programming --- Machine learning --- Stochastic gradient descent --- Deep neural networks --- Matrix completion --- Linear and polynomial regression --- Open Access --- Mathematical & statistical software --- Stochastics

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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.

Engineering. --- Mathematical statistics. --- Probabilities. --- Statistics. --- Signal, Image and Speech Processing. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Probability Theory and Stochastic Processes. --- Probability and Statistics in Computer Science. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Probability --- Statistical inference --- Mathematics --- Statistics, Mathematical --- Construction --- Econometrics --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Statistics --- Probabilities --- Sampling (Statistics) --- Industrial arts --- Technology --- Distribution (Probability theory. --- Computer science. --- Informatics --- Science --- Distribution functions --- Frequency distribution --- Characteristic functions --- Signal processing. --- Image processing. --- Speech processing systems. --- Statistics . --- Computational linguistics --- Electronic systems --- Information theory --- Modulation theory --- Oral communication --- Speech --- Telecommunication --- Singing voice synthesizers --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication)

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This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms.

Information storage and retrieva. --- Artificial intelligence. --- Computer science. --- Information Storage and Retrieval. --- Artificial Intelligence. --- Probability and Statistics in Computer Science. --- Information Systems Applications (incl. Internet). --- Informatics --- Science --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Information storage and retrieval systems. --- Automatic data storage --- Automatic information retrieval --- Automation in documentation --- Computer-based information systems --- Data processing systems --- Data storage and retrieval systems --- Discovery systems, Information --- Information discovery systems --- Information processing systems --- Information retrieval systems --- Machine data storage and retrieval --- Mechanized information storage and retrieval systems --- Computer systems --- Electronic information resources --- Data libraries --- Digital libraries --- Information organization --- Information retrieval --- Information storage and retrieval. --- Mathematical statistics. --- Application software. --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Computer science --- Information storage and retrieval --- Artificial intelligence --- Mathematical statistics --- Application software