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Dataclysm: Who We Are (When We Think No One's Looking) by Christian Rudder

By Christian Rudder

An audacious, irreverent research of human behavior--and a primary examine a revolution within the making

Our own info has been used to undercover agent on us, rent and hearth us, and promote us stuff we don't desire. In Dataclysm, Christian Rudder makes use of it to teach us who we actually are.

for hundreds of years, we've depended on polling or small-scale lab experiments to review human habit. this present day, a brand new technique is feasible. As we are living extra of our lives on-line, researchers can ultimately become aware of us at once, in sizeable numbers, and with no filters. facts scientists became the recent demographers.

during this bold and unique ebook, Rudder explains how fb "likes" can are expecting, with incredible accuracy, a person's sexual orientation or even intelligence; how appealing girls obtain exponentially extra interview requests; and why you need to have haters to be sizzling. He charts the increase and fall of America's so much reviled notice via Google seek and examines the hot dynamics of collaborative rage on Twitter. He indicates how humans convey themselves, either privately and publicly. what's the least Asian factor you could say? Do humans shower extra in Vermont or New Jersey? What do black girls take into consideration Simon & Garfunkel? (Hint: they don't take into consideration Simon & Garfunkel.) Rudder additionally lines human migration over the years, exhibiting how teams of individuals stream from sure small cities to a similar substantial towns around the globe. And he grapples with the problem of holding privateness in a global the place those explorations are possible.

Visually arresting and entire of wit and perception, Dataclysm is a brand new method of seeing ourselves--a impressive alchemy, within which math is made human and numbers develop into the narrative of our time.

From the Hardcover edition.

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Extra info for Dataclysm: Who We Are (When We Think No One's Looking)

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Optimization and Inequalities INTRODUCTION Inequalities play an important role in statistics. Many results, such as the Cramer-Rao inequality, which provides the lower bound of the variance of any estimate of a parameter, are extremely important in theoretical statistics. Some of these inequalities are derived from classical inequalities in mathematics. Inequalities often can be derived from the application of optimization methods. One of the earliest definitive works on inequalities is the book by Hardy, Littlewood, and Polya (1952).

Many other related references for numerical optimization in statistics are given at the end of the book. An important part of a numerical procedure is the algorithm. An algorithm means a rule describing the solution of a problem in a sequence of arithmetic operations with a set of decisions. An important aspect of any algorithm is its accuracy, speed of convergence, and relationship with other algorithms for solving the same problem. We shall describe algorithms for a few optimization procedures.

3. , xn is obtained from a uniform distribution on (0,0). The probability density function is 0 < x < 0, elsewhere. That is, where IA(X) is the indicator function of the set A: 1 0 if x€A, otherwise. The likelihood of the sample is given by where y — max(zi,£2, · · · > ^η)· Note that L is a decreasing function of 0, and hence its maximum occurs at the lower boundary that is at y. Hence, the maximum likelihood estimate of 0 is 0 = y. 24 Optimization Techniques in Statistics Maximum and Minima on Discrete Sets Classical methods of calculus do not apply to functions defined over discrete sets.

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