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Fouad Sabry
Kalman Filters
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1294969Kalman Filtershttps://www.gandhi.com.mx/kalman-filters/phttps://gandhi.vtexassets.com/arquivos/ids/196935/0bb83af7-e02d-4b3d-a658-2e4def656409.jpg?v=6383337722135300007979MXNOne Billion KnowledgeableInStock/Ebooks/1285451Kalman Filters7979https://www.gandhi.com.mx/kalman-filters/phttps://gandhi.vtexassets.com/arquivos/ids/196935/0bb83af7-e02d-4b3d-a658-2e4def656409.jpg?v=638333772213530000InStockMXN99999DIEbook20236610000480081_W3siaWQiOiJhMzEzMDEyYS00Mzg2LTQ1N2YtYTkzNC02M2I3YWViM2QzZWQiLCJsaXN0UHJpY2UiOjg1LCJkaXNjb3VudCI6MCwic2VsbGluZ1ByaWNlIjo4NSwiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6IklwcCIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjQtMDUtMjBUMTk6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d6610000480081_<p><strong>What Is Kalman Filters</strong></p><p>An algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, Kalman filtering is also known as linear quadratic estimation (LQE), and it produces estimates of unknown variables that tend to be more accurate than those that are based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. This is accomplished by estimating a joint probability distribution over the variables for each timeframe. Rudolf E. Kálmán, who was a significant contributor to the development of the theory behind the filter, is honored with the naming of the device.</p><p><strong>How You Will Benefit</strong></p><p>(I) Insights, and validations about the following topics:</p><p>Chapter 1: Kalman filter</p><p>Chapter 2: Weighted arithmetic mean</p><p>Chapter 3: Multivariate random variable</p><p>Chapter 4: Covariance</p><p>Chapter 5: Covariance matrix</p><p>Chapter 6: Expectation-maximization algorithm</p><p>Chapter 7: Minimum mean square error</p><p>Chapter 8: Recursive least squares filter</p><p>Chapter 9: Linear-quadratic-Gaussian control</p><p>Chapter 10: Extended Kalman filter</p><p>(II) Answering the public top questions about kalman filters.</p><p>(III) Real world examples for the usage of kalman filters in many fields.</p><p><strong>Who This Book Is For</strong></p><p>Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of kalman filters.</p><p><strong>What is Artificial Intelligence Series</strong></p><p>The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field.<br />The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.</p>...6610000480081_One Billion Knowledgeablelibro_electonico_509ff329-44b5-3ce3-9d2c-7175629f3a2d_6610000480081;6610000480081_6610000480081Fouad SabryInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/content2connect_drm-epub-0ca160d2-cc4e-44f0-b963-783d2c563c09.epub2023-06-27T00:00:00+00:00One Billion Knowledgeable