book explains how to generate an adequate description of uncertainty, how to justify
semiheuristic algorithms for processing uncertainty, and how to make these algorithms
more computationally efficient. It explains in what sense the existing approach to
uncertainty as a combination of random and systematic components is only an
approximation, presents a more adequate three-component model with an additional
periodic error component, and explains how uncertainty propagation techniques can
be extended to this model. The book provides a justification for a practically efficient
heuristic technique (based on fuzzy decision-making). It explains how the computational
complexity of uncertainty processing can be reduced. The book also shows how to
take into account that in real life, the information about uncertainty is often only
partially known, and, on several practical examples, explains how to extract the missing
information about uncertainty from the available data.
This book provides a useful source for researchers, scientists and engineers who in their daily work are required to deal with problems of measurement and signal processing and can also be helpful to undergraduate students of electrical engineering.
Blending the informed analysis of The Signal and the Noise with the instructive iconoclasm of Think Like a Freak, a fascinating, illuminating, and witty look at what the vast amounts of information now instantly available to us reveals about ourselves and our world—provided we ask the right questions.
By the end of an average day in the early twenty-first century, human beings searching the internet will amass eight trillion gigabytes of data. This staggering amount of information—unprecedented in history—can tell us a great deal about who we are—the fears, desires, and behaviors that drive us, and the conscious and unconscious decisions we make. From the profound to the mundane, we can gain astonishing knowledge about the human psyche that less than twenty years ago, seemed unfathomable.
Everybody Lies offers fascinating, surprising, and sometimes laugh-out-loud insights into everything from economics to ethics to sports to race to sex, gender and more, all drawn from the world of big data. What percentage of white voters didn’t vote for Barack Obama because he’s black? Does where you go to school effect how successful you are in life? Do parents secretly favor boy children over girls? Do violent films affect the crime rate? Can you beat the stock market? How regularly do we lie about our sex lives and who’s more self-conscious about sex, men or women?
Investigating these questions and a host of others, Seth Stephens-Davidowitz offers revelations that can help us understand ourselves and our lives better. Drawing on studies and experiments on how we really live and think, he demonstrates in fascinating and often funny ways the extent to which all the world is indeed a lab. With conclusions ranging from strange-but-true to thought-provoking to disturbing, he explores the power of this digital truth serum and its deeper potential—revealing biases deeply embedded within us, information we can use to change our culture, and the questions we’re afraid to ask that might be essential to our health—both emotional and physical. All of us are touched by big data everyday, and its influence is multiplying. Everybody Lies challenges us to think differently about how we see it and the world.
The text deals with problems of optimal control such as meeting given performance criteria, and stabilization, extending them to neutral stochastic difference Volterra equations. In particular, it contrasts the difference analogues of solutions to optimal control and optimal estimation problems for stochastic integral Volterra equations with optimal solutions for corresponding problems in stochastic difference Volterra equations.
Optimal Control of Stochastic Difference Volterra Equations commences with an historical introduction to the emergence of this type of equation with some additional mathematical preliminaries. It then deals with the necessary conditions for optimality in the control of the equations and constructs a feedback control scheme. The approximation of stochastic quasilinear Volterra equations with quadratic performance functionals is then considered. Optimal stabilization is discussed and the filtering problem formulated. Finally, two methods of solving the optimal control problem for partly observable linear stochastic processes, also with quadratic performance functionals, are developed.
Integrating the author’s own research within the context of the current state-of-the-art of research in difference equations, hereditary systems theory and optimal control, this book is addressed to specialists in mathematical optimal control theory and to graduate students in pure and applied mathematics and control engineering.
After an initial introduction of 2-D systems and the ideas of linear repetitive processes, the text is divided into two parts detailing:
· General theory and methods of analysis and optimal synthesis for 2-D systems; and
· Application of the general theory to the particular case of differential/discrete linear repetitive processes.
The methods developed provide a framework for stability and performance analysis, optimal and robust controller and filter design and model approximation for the systems considered. Solutions to the design problems are couched in terms of linear matrix inequalities.
For readers interested in the state of the art in linear filtering, control and model reduction, Filtering and Control for Classes of Two-Dimensional Systems will be a useful reference for exploring the field of 2-D systems either from a purely theoretical research perspective or from the point of view of a multitude of potential applications including image processing, and the study of seismographic data or thermal processes.
"Ariely not only gives us a great read; he also makes us much wiser."
—George Akerlof, 2001 Nobel Laureate in Economics
—New York Times Book Review
Why do our headaches persist after we take a one-cent aspirin but disappear when we take a fifty-cent aspirin? Why do we splurge on a lavish meal but cut coupons to save twenty-five cents on a can of soup?
When it comes to making decisions in our lives, we think we're making smart, rational choices. But are we?
In this newly revised and expanded edition of the groundbreaking New York Times bestseller, Dan Ariely refutes the common assumption that we behave in fundamentally rational ways. From drinking coffee to losing weight, from buying a car to choosing a romantic partner, we consistently overpay, underestimate, and procrastinate. Yet these misguided behaviors are neither random nor senseless. They're systematic and predictable—making us predictably irrational.