LIBRARY OF CONGRESS CATALOGING IN PUBLICATION DATA. Silver, Nate. The signal and the noise: why most predictions fail but some don't / Nate Silver. The New York Times Book Review Nate Silver built an innovative system for predicting The Signal and the Noise and millions of other books are available for. Editorial Reviews. raudone.info Review. site Best Books of the Month, September The Signal and the Noise, Nate Silver's brilliant and elegant tour of the modern science-slash-art of forecasting, shows what happens when Big.
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PDF | On Feb 1, , Ken Simonson and others published Nate Silver, The Signal and the Noise: Why So Many Predictions Fail—But Some. Nate Silver's The Signal and the Noise: Why So Many. Predictions Fail but Some Don't. By Donna Suchy. Donna Suchy is the principal patent attorney at. During the presidential election, the forecaster Nate Silver did something that I imagine happens rarely in the world of po- litical bloggers – he became part .
The Princeton Gerrymandering Project
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.
Some highlights from Nate Silver's "The Signal and the Noise"
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.
The credit rating agencies selling AAA ratings on mortgage securities. The financial melt-down caused by high leverage in the banking sector.
Homeowners and taxpayers made money on paper at least in the short term but lost in the long term, but the bankers took home bonuses that they still have. As we discussed, this was a system-wide tool for building a money machine.
The financial melt-down was incidental, but the leverage was intentional. It bumped up the risk and thus, in good times, the bonuses.
Filling in the blanks: The math behind Nate Silver's "The Signal and the Noise"
Economists are only putatively trying to predict the recovery. They get paid the big bucks, and they are granted authority and power in part to give consumers confidence, which they presumably hope will lead to a robust economy.
Cause and effect get confused Silver confuses cause and effect. We had bad models because of a corrupt and criminally fraudulent financial system.
Why does he make this mistake? Actually, he made an exception for stock analysts. But he falls prey to some of the very mistakes he warns about in his book, namely over-confidence and over-generalization. Most of the people in the field are honestly trying their best.
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But their intentions are kind of irrelevant. Silver ignores politics and loves experts Silver chooses to focus on individuals working in a tight competition and their motives and individual biases, which he understands and explains well.
Silver believes in the need for extensive data sets, preferably collected over long periods of time, from which one can then use statistical techniques to incrementally change probabilities up or down relative to prior data.
This "Bayesian" approach is named for the 18th century minister Thomas Bayes who discovered a simple formula for updating probabilities using new data. For Silver, the well-known method needs revitalizing as a broader paradigm for thinking about uncertainty, founded on learning and understanding gained incrementally, rather than through any single set of observations or an ideal model summarized by just a few key parameters.
Part of that learning is the informal process of changing assumptions or the modeling approach, in the spirit of a craft whose goal is to devise the best betting odds on well-defined future events and their outcomes.
Criticism and commentary[ edit ] Climate scientist Michael E.
Mann criticized the book for analyzing the "hard science" physical phenomena of climate trends with the same approach as used to analyze the social phenomena of voter preferences, which he characterized as "laden with subjective and untestable assumptions".
However, he purposefully leaves out the mathematics. In , after his triumph of predicting the outcome of the last two presidential elections and selling his "fivethirtyeight" blog to the New York Times, Nate Silver accomplished what is almost impossible.
In his recent book The Signal and the Noise, he correctly describes the discipline of making predictions, without explicitly invoking the math.Views Read Edit View history. Slovic P. Most modern predictive techniques, such as machine learning, are Bayesian in spirit.
It's full of interesting cases, builds intuition, and is a readable example of Bayesian thinking. The models cover a diversity of areas from baseball to politics, from earthquakes to finance, from climate science to chess. This book was first published in , at a time when Big Data or if you prefer, big data was only beginning to receive the attention it deserves as a better way to use analytics within and beyond the business world.
It implies that we must think differently about our ideas [predictions, for example] -- and how to test them. Inductive reasoning, Bayesian reasoning There is a definite Bayesian perspective that underlies the ability to make good predictions.
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