Products related to Inference:
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Inference and Learning from Data: Volume 2 : Inference
This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference.This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning.A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code.Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
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Inference and Learning from Data
This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference.The first volume, Foundations, establishes core topics in inference and learning, and prepares readers for studying their practical application.The second volume, Inference, introduces readers to cutting-edge techniques for inferring unknown variables and quantities.The final volume, Learning, provides a rigorous introduction to state-of-the-art learning methods.A consistent structure and pedagogy is employed throughout all three volumes to reinforce student understanding, with over 1280 end-of-chapter problems (including solutions for instructors), over 600 figures, over 470 solved examples, datasets and downloadable Matlab code.Unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
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Computer Vision : Models, Learning, and Inference
This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme.It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data.With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems.Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. • Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry • A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition and object tracking • More than 70 algorithms are described in sufficient detail to implement • More than 350 full-color illustrations amplify the text • The treatment is self-contained, including all of the background mathematics • Additional resources at www.computervisionmodels.com
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Information Theory, Inference and Learning Algorithms
Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography.The book introduces theory in tandem with applications.Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction.Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks.Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast.Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses.It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.
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What is inference in linear regression?
Inference in linear regression refers to the process of drawing conclusions about the relationships between variables based on the estimated coefficients of the regression model. It involves testing hypotheses about the significance of these coefficients and making predictions about the dependent variable. Inference helps us understand the strength and direction of the relationships between the independent and dependent variables, as well as the overall fit of the model to the data. It is an important aspect of linear regression analysis that allows us to make informed decisions and interpretations based on the statistical results.
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What exactly is a mathematical inference in mathematics and computer science?
A mathematical inference in mathematics and computer science is the process of drawing conclusions or making predictions based on existing information or data. In mathematics, this often involves using logical reasoning and mathematical principles to make deductions or prove the validity of a statement. In computer science, mathematical inference can be used in areas such as artificial intelligence and machine learning to make predictions or decisions based on patterns and data. Overall, mathematical inference is a fundamental concept in both fields that allows for the application of logic and reasoning to solve problems and make decisions.
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How are logical inference, the Gentzen calculus, and De Morgan's laws correctly derived?
Logical inference is the process of deriving new information from existing knowledge using valid reasoning. The Gentzen calculus is a formal system for representing and manipulating logical inference in a rigorous way. De Morgan's laws, which describe the relationships between logical conjunction and disjunction, can be correctly derived using the rules of the Gentzen calculus, which ensures that the inference process is sound and valid. By following the rules of the Gentzen calculus, one can systematically derive De Morgan's laws and other logical principles in a mathematically rigorous manner.
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How does growth progress?
Growth progresses through a series of stages, starting from infancy and continuing through childhood, adolescence, and adulthood. During these stages, individuals experience physical, cognitive, emotional, and social development. Factors such as genetics, environment, nutrition, and experiences all play a role in shaping an individual's growth trajectory. As individuals grow, they acquire new skills, knowledge, and perspectives that contribute to their overall development and well-being.
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Inference and Learning from Data: Volume 3 : Learning
This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference.This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks.A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code.Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
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Causal Inference
A nontechnical guide to the basic ideas of modern causal inference, with illustrations from health, the economy, and public policy. Which of two antiviral drugs does the most to save people infected with Ebola virus?Does a daily glass of wine prolong or shorten life? Does winning the lottery make you more or less likely to go bankrupt?How do you identify genes that cause disease? Do unions raise wages? Do some antibiotics have lethal side effects? Does the Earned Income Tax Credit help people enter the workforce?Causal Inference provides a brief and nontechnical introduction to randomized experiments, propensity scores, natural experiments, instrumental variables, sensitivity analysis, and quasi-experimental devices.Ideas are illustrated with examples from medicine, epidemiology, economics and business, the social sciences, and public policy.
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Statistical Inference
This classic textbook builds theoretical statistics from the first principles of probability theory.Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and natural extensions, and consequences, of previous concepts.It covers all topics from a standard inference course including: distributions, random variables, data reduction, point estimation, hypothesis testing, and interval estimation. Features The classic graduate-level textbook on statistical inferenceDevelops elements of statistical theory from first principles of probabilityWritten in a lucid style accessible to anyone with some background in calculusCovers all key topics of a standard course in inferenceHundreds of examples throughout to aid understandingEach chapter includes an extensive set of graduated exercisesStatistical Inference, Second Edition is primarily aimed at graduate students of statistics, but can be used by advanced undergraduate students majoring in statistics who have a solid mathematics background.It also stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures, while less focused on formal optimality considerations. This is a reprint of the second edition originally published by Cengage Learning, Inc. in 2001.
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Statistical Inference
This book builds theoretical statistics from the first principles of probability theory.Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts.Intended for first-year graduate students, this book can be used for students majoring in statistics who have a solid mathematics background.It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.
Price: 71.99 £ | Shipping*: 0.00 £
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How does language development progress?
Language development progresses in stages, starting with cooing and babbling in infancy, then moving on to single words, two-word phrases, and eventually more complex sentences. Children learn language through exposure to spoken language, imitation, and reinforcement from caregivers. As they grow, they continue to expand their vocabulary, grammar, and communication skills through interactions with others and practice in various social contexts. Language development is a gradual process that is influenced by both biological factors and environmental experiences.
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How does human development progress?
Human development progresses through a series of stages that are influenced by both biological and environmental factors. These stages include physical, cognitive, emotional, and social development. As individuals grow and mature, they acquire new skills, knowledge, and experiences that shape their understanding of the world and their place in it. Development is a lifelong process that continues from infancy through adulthood and is influenced by a combination of genetics, environment, and personal experiences.
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How does sensory development progress?
Sensory development progresses through a series of stages, beginning with basic sensory experiences such as touch, taste, and smell in infancy. As children grow, they develop more refined sensory abilities, such as improved vision and hearing, and begin to integrate sensory information to make sense of their environment. This process continues into adolescence and adulthood, with individuals becoming more adept at interpreting and responding to sensory stimuli. Overall, sensory development progresses from basic sensory experiences to more complex and integrated sensory abilities as individuals mature.
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How does the growth progress?
The growth progress is a gradual and continuous process that involves an increase in size, development, and maturity. It can be observed in various aspects of life, such as physical, emotional, intellectual, and professional growth. This progress is often influenced by factors such as genetics, environment, nutrition, and experiences. As individuals age, they go through different stages of growth, each with its own set of milestones and challenges. Overall, growth progress is a dynamic and ongoing journey that shapes individuals into who they are.
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