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Nate Silver, for readers who do not know, write FiveThirtyEight blog used to be independent, but it is now a part of The New York Times. FiveThirtyEight bread to predict the outcome of the U.S. elections, the polling stations and other information. The recent U.S. presidential election, FiveThirtyEight and other quantitative political blog received a lot of press predict a narrow victory sure, for President Obama, as many of the non-quantitative political pundits, the election said was too close to call, or even (if they are conservative were) says victory over challenger Mitt Romney. And FiveThirtyEight forecast model does not only predict the overall result is correct, it is correctly predicted the winner in all 50 states, and the forecasts for the percentage of votes for each candidate in each state were both accurate and precise. This was not the first Nate Silver predictive success, or outside the polling forecasts. As a baseball fan, I'm also with his previous work, develop a Baseball Prospectus PECOTA, a model for the future development of Major League Baseball players predict familiar. PECOTA was the first of its kind and is still very successful. He studied economist, spent some time as a consultant accounting firmand (what seems to have a very formative experience for him) spent a lot of time and made a lot of money playing online poker. Now he has written a book on the general problem of predicting the future. In particular, the way we have for the most part it is really bad, with a few exceptions. He talks about his own experiences to predict the election, the performance of baseball, and poker (Poker is well contained, to make predictions about what cards could keep the other, based on the limited information make). And he also talks about the history of prophecy all sorts of other areas, the weather, hurricane tracks (two unusually predictor of success stories), earthquakes, computer chess, stock market, economy, gambling, sports, climate change, terrorism and more. I have the book very much and I would recommend it to anyone interested in the comprehensive problem in order to make predictions. And it is a widespread problem; one of the real strengths of the book is, and if they succeed, to what extent silver threw fail its net profit insights into the predictions. He holds characteristics of the system tries to predict (for example, it is chaotic). He talks about the characteristics of the available data and background information (for example, a system of mechanically well understood how much information we have, and what variables). He talks about the characteristics of people who are trying to predict circuit (for example, what incentives they have to make good predictions, it warns the common cognitive distortions) to make. He talks about what kind of people trying to make predictions (for example, to, qualitative and quantitative forecasts the time and place where a particular event occurs) are. And he talks about the different techniques to generate predictions (eg, the betting market, mechanistic models, statistical models). The book is full of interesting nuggets, I do not know. It is also very well written and engaging. And I did not find any errors in the conversations about the things that I know something about (baseball, computer chess, stock market, economy), which is reassuring.The general picture is that it is to make no universal recipe, good predictions. Good prognosis involves a lot of good judgment, and I mean to decide how to weigh the various general considerations of the particular case. Few things are always useful, as good mechanistic information (which we have weather, hurricanes and poker), a large historical database of cases similar to those we are trying to predict (which we in baseball have), and all sources of uncertainty and to recognize errors. And some things are always helpful, the most important thing we tend to "pattern", where there is nothing, and so overfit see data and overconfident predictions. But in between there are a lot of things that under certain circumstances helpful, but useless in others. Take, for example, more computing power has helped forecasters, who long just right for a mechanistic model of the atmosphere have weather, but it lacked the ability to simulate it in a sufficiently fine spatial resolutions. More computing power has also helped to computer chess. But it did not help prediction of earthquakes, because we lack the ability to even a proper mechanistic model, let alone to parameterize have it. And with the knowledge of more predictor variables can sometimes be helpful, but often it just means more noise that you filter out to the need to find an increased risk of over-fitting the signal. "Big Data" usually just means the larger the haystack, you have to search to find the same needle. (Graduate students to listen to this last lesson when designing your own projects! Do not measure a lot of parameters, just because you can or because you feel more information is always better!)Watch The Signal Online FreeWatch The Signal OnlineWatch The Signal Full Movie