Products related to Inference:
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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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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 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.
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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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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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What is a specialist in application development for information technology?
A specialist in application development for information technology is a professional who specializes in designing, creating, and maintaining software applications that meet the specific needs of an organization. They have expertise in programming languages, software development methodologies, and database management. These specialists work closely with clients and stakeholders to understand their requirements and develop customized solutions to improve efficiency, productivity, and overall business operations. They are responsible for ensuring that the applications are user-friendly, secure, and scalable to meet the evolving needs of the organization.
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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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Innovation in Information Technology
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Model Selection and Multimodel Inference : A Practical Information-Theoretic Approach
We wrote this book to introduce graduate students and research workers in various scienti?c disciplines to the use of information-theoretic approaches in the analysis of empirical data.These methods allow the data-based selection of a “best” model and a ranking and weighting of the remaining models in a pre-de?ned set.Traditional statistical inference can then be based on this selected best model.However, we now emphasize that information-theoretic approaches allow formal inference to be based on more than one model (m- timodel inference).Such procedures lead to more robust inferences in many cases, and we advocate these approaches throughout the book.The second edition was prepared with three goals in mind.First, we have tried to improve the presentation of the material.Boxes now highlight ess- tial expressions and points.Some reorganization has been done to improve the ?ow of concepts, and a new chapter has been added.Chapters 2 and 4 have been streamlined in view of the detailed theory provided in Chapter 7.S- ond, concepts related to making formal inferences from more than one model (multimodel inference) have been emphasized throughout the book, but p- ticularly in Chapters 4, 5, and 6.Third, new technical material has been added to Chapters 5 and 6.Well over 100 new references to the technical literature are given.These changes result primarily from our experiences while giving several seminars, workshops, and graduate courses on material in the ?rst e- tion.
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Nonparametric Statistical Inference
Praise for previous editions:"… a classic with a long history." – Statistical Papers"The fact that the first edition of this book was published in 1971 … [is] testimony to the book’s success over a long period." – ISI Short Book Reviews"… one of the best books available for a theory course on nonparametric statistics. … very well written and organized … recommended for teachers and graduate students." – Biometrics"… There is no competitor for this book and its comprehensive development and application of nonparametric methods.Users of one of the earlier editions should certainly consider upgrading to this new edition." – Technometrics"… Useful to students and research workers … a good textbook for a beginning graduate-level course in nonparametric statistics." – Journal of the American Statistical AssociationSince its first publication in 1971, Nonparametric Statistical Inference has been widely regarded as the source for learning about nonparametrics.The Sixth Edition carries on this tradition and incorporates computer solutions based on R.Features Covers the most commonly used nonparametric procedures States the assumptions, develops the theory behind the procedures, and illustrates the techniques using realistic examples from the social, behavioral, and life sciences Presents tests of hypotheses, confidence-interval estimation, sample size determination, power, and comparisons of competing procedures Includes an Appendix of user-friendly tables needed for solutions to all data-oriented examples Gives examples of computer applications based on R, MINITAB, STATXACT, and SAS Lists over 100 new referencesNonparametric Statistical Inference, Sixth Edition, has been thoroughly revised and rewritten to make it more readable and reader-friendly.All of the R solutions are new and make this book much more useful for applications in modern times.It has been updated throughout and contains 100 new citations, including some of the most recent, to make it more current and useful for researchers.
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What is a specialist in information technology in application development?
A specialist in information technology in application development is a professional who focuses on creating and maintaining software applications. They have expertise in programming languages, software development methodologies, and application design. Their role involves analyzing user needs, designing application solutions, writing code, testing and debugging applications, and ensuring that they meet the required specifications and standards. These specialists often work closely with other IT professionals and stakeholders to deliver high-quality, user-friendly applications.
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Why is information and communication technology referred to as useful energy?
Information and communication technology is referred to as useful energy because it enables the efficient transmission and processing of information, leading to increased productivity and effectiveness in various tasks. Just like energy is essential for powering machines and devices, ICT provides the necessary tools and resources for communication, data storage, and problem-solving. It plays a crucial role in powering modern economies and societies by facilitating connectivity, innovation, and collaboration.
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What does an electronics technician specializing in information and communication technology earn?
An electronics technician specializing in information and communication technology can earn an average salary of around $60,000 to $80,000 per year, depending on factors such as experience, location, and employer. With additional certifications or advanced degrees, they may be able to command higher salaries. Overall, this field offers competitive wages and opportunities for career growth as technology continues to advance.
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How much does an electronics technician for information and communication technology earn?
The salary of an electronics technician for information and communication technology can vary depending on factors such as experience, location, and employer. On average, in the United States, an electronics technician in this field can earn between $40,000 to $70,000 per year. However, with more experience and specialized skills, technicians can earn higher salaries, potentially exceeding $80,000 annually. It's important to research specific companies and regions to get a more accurate idea of salary expectations.
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