Game Theory is the mathematical formalization of interactive decision-making - it assumes that each player's goal is to maximize his/her benefit, whatever it may be. Players may be friends, foes, political parties, states, or any entity that behaves interactively, whether collectively or individually. One of the problems with game analysis is the fact that, as a player, it's very hard to know what would benefit each of the other players; some of us are not even clear about our own goals or what might actually benefit us. Haim Shapira uses multiple examples to explain what Game Theory is and how the different interactions between decision-makers can play out. In this book you will: Meet the Nobel Laureate John F Nash and familiarize yourself with his celebrated equilibrium Learn the basic ideas of the art of negotiation Visit the gladiators' ring and apply for a coaching position Build an airport and divide inheritance Issue ultimatums and learn to trust

    Included are step-by-step instructions on how to carry out Bayesian data analyses.Download Link : readbux.com/download?i=0124058884            0124058884 Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan PDF by John Kruschke

    All things are possible, only one thing actually happens; everything else is in the realm of probability. The twin disciplines of probability and statistics underpin every modern science and sketch the shape of all purposeful group activity- politics, economics, medicine, law, sports-giving humans a handle on the essential uncertainty of their existence. Yet while we are all aware of the hard facts, most of us still refuse to take account of probability-preferring to drive, not fly; buying into market blips; smoking cigarettes; denying we will ever age. There are some people, though-gamblers, risk buyers, forensic experts, doctors, strategists- who find probability's mass of incomplete uncertainties delightful and revelatory. "Chances Are" is their story. Combining philosophical and historical background with portraits of the men and women who command the forces of probability, this engaging, wide-ranging, and clearly written volume will be welcomed not only by the proven audiences for popular books like "E=MC2" and "The Golden Ratio" but by anyone interested in the workings of fate.

    Peter Bernstein has written a comprehensive history of man's efforts to understand risk and probability, beginning with early gamblers in ancient Greece, continuing through the 17th-century French mathematicians Pascal and Fermat and up to modern chaos theory. Along the way he demonstrates that understanding risk underlies everything from game theory to bridge-building to winemaking.

    Petr Beckmann holds up this mirror, giving the background of the times when pi made progress -- and also when it did not, because science was being stifled by militarism or religious fanaticism.

    Mandelbrot, one of the century's most influential mathematicians, is world-famous for making mathematical sense of a fact everybody knows but that geometers from Euclid on down had never assimilated: Clouds are not round, mountains are not cones, coastlines are not smooth. To these classic lines we can now add another example: Markets are not the safe bet your broker may claim. In his first book for a general audience, Mandelbrot, with co-author Richard L. Hudson, shows how the dominant way of thinking about the behavior of markets-a set of mathematical assumptions a century old and still learned by every MBA and financier in the world-simply does not work. As he did for the physical world in his classic The Fractal Geometry of Nature, Mandelbrot here uses fractal geometry to propose a new, more accurate way of describing market behavior. The complex gyrations of IBM's stock price and the dollar-euro exchange rate can now be reduced to straightforward formulae that yield a far better model of how risky they are. With his fractal tools, Mandelbrot has gotten to the bottom of how financial markets really work, and in doing so, he describes the volatile, dangerous (and strangely beautiful) properties that financial experts have never before accounted for. The result is no less than the foundation for a new science of finance.

    With it you can prime yourself with the key concepts of statistics before getting involved in the associated calculations. Using words and diagrams instead of figures, formulae and equations, Derek Rowntree makes statistics accessible to those who are non-mathematicians. And just to get you into the spirit of things. Rowntree has included questions in his argument; answer them as you go and you will be able to tell how far you have mastered the subject.

    Like its sister textbook, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is enhanced by a cast of characters to help the reader on their way, hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more.

    Designed for two-semester, beginning graduate courses in Mathematical Statistics, and for senior undergraduate Mathematics, Statistics, and Actuarial Science majors, this text retains its ongoing features and continues to provide students with background material.

    Beginning with the background and very nature of probability theory, the book then proceeds through sample spaces, combinatorial analysis, fluctuations in coin tossing and random walks, the combination of events, types of distributions, Markov chains, stochastic processes, and more. The book's comprehensive approach provides a complete view of theory along with enlightening examples along the way.

    This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems (including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter) in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. Time Series Analysis fills an important need for a textbook that integrates economic theory, econometrics, and new results.The book is intended to provide students and researchers with a self-contained survey of time series analysis. It starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers.-- "Journal of Economics"

    This is the classic introduction for the educated lay reader to the richly diverse world of mathematics: its history, philosophy, principles, and personalities.

    The aim of a course in real analysis should be to challenge and improve mathematical intuition rather than to verify it. The philosophy of this book is to focus attention on questions which give analysis its inherent fascination.

    Most of the time this math works quietly behind the scenes . . . until it doesn't. All sorts of seemingly innocuous mathematical mistakes can have significant consequences.Math is easy to ignore until a misplaced decimal point upends the stock market, a unit conversion error causes a plane to crash, or someone divides by zero and stalls a battleship in the middle of the ocean.Exploring and explaining a litany of glitches, near misses, and mathematical mishaps involving the internet, big data, elections, street signs, lotteries, the Roman Empire, and an Olympic team, Matt Parker uncovers the bizarre ways math trips us up, and what this reveals about its essential place in our world. Getting it wrong has never been more fun.

    We want to be able to figure out who will win an election, if the stock market will crash, or if a suspect definitely committed a crime. But the odds are not in our favor. Life is full of uncertainty --- indeed, scientific advances indicate that the universe might be fundamentally inexact --- and humans are terrible at guessing. When asked to predict the outcome of a chance event, we are almost always wrong.Thankfully, there is hope. As award-winning mathematician Ian Stewart reveals, over the course of history, mathematics has given us some of the tools we need to better manage the uncertainty that pervades our lives. From forecasting, to medical research, to figuring out how to win Let's Make a Deal, Do Dice Play God? is a surprising and satisfying tour of what we can know, and what we never will.