"One of the more momentous books of the decade." -The New York Times Book Review Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of the website FiveThirtyEight. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the “prediction paradox”: The more humility we have about our ability to make predictions, the more successful we can be in planning for the future. In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good—or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary—and dangerous—science. Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise. With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver’s insights are an essential read.
the signal and the noise
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The Signal and the Noise: Why So Many Predictions Fail — but Some Don’t (2012), considers the common shortcomings statisticians face when attempting to make predictions. While bad predictions can lead to setbacks and false beliefs, accurate predictions can advance scientific knowledge, government efficiency, the quality of education, and other social projects and goals… Purchase this in-depth summary to learn more.
The Signal and the Noise …in 30 Minutes is the essential guide to quickly understanding the fundamental components of prediction outlined in Nate Silver’s bestselling book, The Signal and the Noise: Why So Many Predictions Fail ¬– but Some Don’t. In The Signal and the Noise bestselling author, political analyst, and statistician Nate Silver investigates the fundamentals of forecasting and answers why too much information can be misleading. Exploring a variety of fields, ranging from politics to poker to Wall Street and global warming, Silver explores why some forecasts are successful and, perhaps more telling, why so many fail. Stressing the importance of acknowledging personal bias, Silver posits that better forecasters possess a superior understanding of uncertainty and are driven by truth and humility while overconfidence can lead to failure. Presenting a framework for what constitutes a good forecast, Silver provides insight and tools for understanding how to successfully utilize Big Data and decipher meaningful signals from random noise.
Digital signal processing plays a central role in the development of modern communication and information processing systems. The theory and application of signal processing is concerned with the identification, modelling and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete and noisy and therefore noise reduction, the removal of channel distortion, and replacement of lost samples are important parts of a signal processing system. The fourth edition of Advanced Digital Signal Processing and Noise Reduction updates and extends the chapters in the previous edition and includes two new chapters on MIMO systems, Correlation and Eigen analysis and independent component analysis. The wide range of topics covered in this book include Wiener filters, echo cancellation, channel equalisation, spectral estimation, detection and removal of impulsive and transient noise, interpolation of missing data segments, speech enhancement and noise/interference in mobile communication environments. This book provides a coherent and structured presentation of the theory and applications of statistical signal processing and noise reduction methods. Two new chapters on MIMO systems, correlation and Eigen analysis and independent component analysis Comprehensive coverage of advanced digital signal processing and noise reduction methods for communication and information processing systems Examples and applications in signal and information extraction from noisy data Comprehensive but accessible coverage of signal processing theory including probability models, Bayesian inference, hidden Markov models, adaptive filters and Linear prediction models Advanced Digital Signal Processing and Noise Reduction is an invaluable text for postgraduates, senior undergraduates and researchers in the fields of digital signal processing, telecommunications and statistical data analysis. It will also be of interest to professional engineers in telecommunications and audio and signal processing industries and network planners and implementers in mobile and wireless communication communities.
Signals and Systems for Bioengineers guides the reader through the electrical engineering principles that can be applied to biological systems and are therefore important to biomedical studies. The basic engineering concepts that underlie biomedical systems, medical devices, biocontrol, and biosignal analysis are explained in detail. This textbook is perfect for the one-semester bioengineering course usually offered in conjunction with a laboratory on signals and measurements which presents the fundamentals of systems and signal analysis. The target course occupies a pivotal position in the bioengineering curriculum and will play a critical role in the future development of bioengineering students. Reorganized to emphasize signal and system analysis Increased coverage of time-domain signal analysis Expanded coverage of biomeasurement, using examples in ultrasound and electrophysiology New applications in biocontrol, with examples from physiological systems modeling such as the respiratory system Double the number of Matlab and non-Matlab exercises to provide ample practice solving problems - by hand and with computational tools More Biomedical and real-world examples More biomedical figures throughout
Random signals and noise are present in many engineering systems and networks. Signal processing techniques allow engineers to distinguish between useful signals in audio, video or communication equipment, and interference, which disturbs the desired signal. With a strong mathematical grounding, this text provides a clear introduction to the fundamentals of stochastic processes and their practical applications to random signals and noise. With worked examples, problems, and detailed appendices, Introduction to Random Signals and Noise gives the reader the knowledge to design optimum systems for effectively coping with unwanted signals. Key features: Considers a wide range of signals and noise, including analogue, discrete-time and bandpass signals in both time and frequency domains. Analyses the basics of digital signal detection using matched filtering, signal space representation and correlation receiver. Examines optimal filtering methods and their consequences. Presents a detailed discussion of the topic of Poisson processes and shot noise. An excellent resource for professional engineers developing communication systems, semiconductor devices, and audio and video equipment, this book is also ideal for senior undergraduate and graduate students in Electronic and Electrical Engineering.
Additive and multiplicative noise in the information signal can significantly limit the potential of complex signal processing systems, especially when those systems use signals with complex phase structure. During the last few years this problem has been the focus of much research, and its solution could lead to profound improvements in applications of complex signals and coherent signal processing. Signal Processing Noise sets forth a generalized approach to signal processing in multiplicative and additive noise that represents a remarkable advance in signal processing and detection theory. This approach extends the boundaries of the noise immunity set by classical and modern signal processing theories, and systems constructed on this basis achieve better detection performance than that of systems currently in use. Featuring the results of the author's own research, the book is filled with examples and applications, and each chapter contains an analysis of recent observations obtained by computer modelling and experiments. Tables and illustrations clearly show the superiority of the generalized approach over both classical and modern approaches to signal processing noise. Addressing a fundamental problem in complex signal processing systems, this book offers not only theoretical development, but practical recommendations for raising noise immunity in a wide range of applications.
Understanding the nature of random signals and noise is critically important for detecting signals and for reducing and minimizing the effects of noise in applications such as communications and control systems. Outlining a variety of techniques and explaining when and how to use them, Random Signals and Noise: A Mathematical Introduction focuses on applications and practical problem solving rather than probability theory. A Firm Foundation Before launching into the particulars of random signals and noise, the author outlines the elements of probability that are used throughout the book and includes an appendix on the relevant aspects of linear algebra. He offers a careful treatment of Lagrange multipliers and the Fourier transform, as well as the basics of stochastic processes, estimation, matched filtering, the Wiener-Khinchin theorem and its applications, the Schottky and Nyquist formulas, and physical sources of noise. Practical Tools for Modern Problems Along with these traditional topics, the book includes a chapter devoted to spread spectrum techniques. It also demonstrates the use of MATLAB® for solving complicated problems in a short amount of time while still building a sound knowledge of the underlying principles. A self-contained primer for solving real problems, Random Signals and Noise presents a complete set of tools and offers guidance on their effective application.
In-depth coverage of instrumentation and measurement from theWiley Encyclopedia of Electrical and Electronics Engineering The Wiley Survey of Instrumentation andMeasurement features 97 articles selected from the WileyEncyclopedia of Electrical and Electronics Engineering, the onetruly indispensable reference for electrical engineers. Together,these articles provide authoritative coverage of the importanttopic of instrumentation and measurement. This collection also, forthe first time, makes this information available to those who donot have access to the full 24-volume encyclopedia. The entireencyclopedia is available online-visitwww.interscience.wiley.com/EEEE for more details. Articles are grouped under sections devoted to the major topicsin instrumentation and measurement, including: Sensors and transducers Signal conditioning General-purpose instrumentation and measurement Electrical variables Electromagnetic variables Mechanical variables Time, frequency, and phase Noise and distortion Power and energy Instrumentation for chemistry and physics Interferometers and spectrometers Microscopy Data acquisition and recording Testing methods The articles collected here provide broad coverage of thisimportant subject and make the Wiley Survey ofInstrumentation and Measurement a vital resource forresearchers and practitioners alike.