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THE SIGNAL AND THE NOISE: WHY SO MANY PREDICTIONS FAIL--BUT SOME DONT PDF, EPUB, EBOOK

Basically, it's hard to predict stuff. The chance of getting a positive mammogram for a woman without cancer. Without any introduction to the subject, he claims Hume is stuck in some 'skeptical shell' that prevents him from understanding the simple, elegant solutions of Bayes. In almost every chapter following this he refers to the way that Bayesian reasoning can be used to strengthen forecasting and to overcome some of the difficulties of predicting in that area. Silver simply crunched the numbers and nailed the outcomes in every state. Vision and taste, for example, are perceptions derived from the brain's ability to discern pattern. It was published in Japanese in November One of my favorite tweets ever I don't read many tweets came from Ken Jennings on election morning of , something along t Reading Nate Silver is like exhaling after holding your breath for a really long time. Had this quote been from the introduction, and had the book given any insight into how to get beyond the platitudes, it would be the book I hoped to read. Silver's book, The Signal and the Noise , was published in September In respect of the financial crisis, he identifies various failures of prediction housing bubble, rating agencies, failure to see how it would cause a global financial crisis, failure to realise how big and deep recession would be which he largely ascribes to over-confidence and inability to forecast out of sample events. Feb 13, Wen rated it it was amazing Shelves: data-science. But, I did find the book fascinating, informative, and chock full calculations juxtaposed against unpredictable elements that could not be foreseen, or against patterns in plain sight, were ignored, all mix together to prove why predictions and forecast often fail, but also, what makes them work! People often tend to ignore items 1 and 3 on the list, leading to very erroneous conclusions. Be very afraid. Nathaniel Read "Nate" Silver born January 13, is an American statistician and writer who analyzes baseball and elections. There was a missed opportunity to spend some time on results from the medical research industry. Also, the explanation of Bayes' theorem was solid, as was the chapter on stocks. As has been noted by others, the number of typographical errors is unacceptable. Just think about the times when you made it out of the path of a tornado, and be thankful for these guys, who must decipher an incredible amount of data and unpredictable patterns, and they must deal with the human element on top of that. Are they good-or just lucky? Loved each and every part of this book. All that being said, be forewarned that most people will find this book extremely boring. The first section of the book, takes a look at the various ways experts make predictions, and how they could miss something like the financial crisis, for example. The majority of chapters in this book are inferior rehashes of arguments and anecdotes from other authors. See more Timeisrhythm. Over-simplification on the one hand and brute-force data crunching on the other can both lead to serious errors. Then I'm jarred out of complacency by a sudden shot from nowhere, in which he says that David Hume, one of the greatest philosophers of the 18th century, is simply too 'daft to understand' probabilistic arguments. He provides examples from Kasparov's chess match with Big Blue, and an interview on poker strategy with Tom Dwan. By summer of that year, after he revealed his identity to his readers, he began to appear as an electoral and political analyst in national print, online, and cable news media. Goodreads Librari Let's start by two weaknesses: At some points it seems good prediction looks like a 'hammer' to see all the problems as 'needles'. While I was searching for the words to describe the book, I have found the perfect description in Chapter 12 the book itself: Heuristics like Occam's razor If I weren't a completist I would have read only the chapters that started going somewhere in the first few pages, as the correlation between the first five pages was. One is the fawning approach to Donald Rumsfeld. But the number of meaningful relationships in the data. That's what I was assuming the book would be. Mar 26, Justin rated it it was ok. The Signal and the Noise: Why So Many Predictions Fail--But Some Dont Writer

If he had even kept on for five more pages he would have found that Hume was defending the very type of probabilistic arguments that Silver said Hume was 'too daft' to understand. These different topics illustrate different statistical principles. 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. Please note that the tricks or techniques listed in this pdf are either fictional or claimed to work by its creator. At any rate, I think the chapters on the financial collapse and global warming should be required reading for everyone, and the rest of it for those who are interested. Interesting at points, but the main message gets swallowed by the noise—almost too much random content. Jan 19, Dave rated it liked it. View all 13 comments. That same year, Silver's predictions of U. The chapter on terrorism was an excellent ending to the book, as it not only tied the concepts together, but it also made apparent the stakes in predicti This was a fun read that tickled the nonfiction part of my brain in pleasant ways. These include the housing bubble, the collapse of the Soviet Union, and the Fukushima disaster. Book about prediction by the author of the political blog, which became particularly famous in the presidential election after the book was written due to the author's high confidence in an Obama victory due to polling evidence in marginals. An admonition like "The more complex you make the model the worse the forecast gets" is equivalent to saying "Never add too much salt to the recipe" Throughout it all, he reminds us that human beings are pattern-seeking animals and that we are just as likely to build patterns where none exist as we are to find the correct patterns and harness their predictive capacity. See all 6 questions about The Signal and the Noise…. Original Title. The Noise Everything else. More filters. Just, turns out I prefer him doing stats in word articles and in person, where he comes across much better. See more Timeisrhythm. But what I was not expecting was the exten I picked up The Signal and the Noise from the library because I thought it would be slightly boring. He calmly points out that some things are predictable and are predicted, using various methods with resultant various success. So,yes, Silver's political forecasting is exceedingly accurate and his writing is hit or miss. I am sure the vast majority of readers will roll a bemused eye at my anger over trivial details like this - but not only does it show that Silver very often doesn't take the time to understand his sources see Michael Mann's critique of Silver's presentation of global warming , but Silver's casual remarks could easily turn a lot of readers off to Hume before they've even read him. Senate races that year. Silver frames issues in terms of Bayes Theorem, which is a centuries-old mathematical formula for determining how probabilities change as new information becomes available. If our appreciation of uncertainty improves, our predictions can get better too. I read some of the other reviews that complained that this book is somewhat meandering, which I can see. Most of my book group ended up awarding only 3-stars. Loved each and every part of this book. Dont Let Me Go by J. This "Bayesian" approach is named for the 18th century minister Thomas Bayes who discovered a simple formula for updating probabilities using new data. I had hoped that the book would draw on the author's experience and give an insight into how to apply this idea in the real world. Four stars, without hesitation. I approached the chapter on climate prediction with some trepidation, wondering if Silver was going to somehow take the position that it was all baloney. The writing is excellent, the graphics helpful and the type not too small. It concluded that most of these findings were likely to fail when applied in the real world. An even greater editorial error is letting the author ramble on again, in some chapters. But I really didn't mind at all. The difficulty in handling large amounts of data is separating the signal from the noise. If you've read Michael Lewis's The Big Short and Moneyball you can skip chapters 1 and 3 and if you've ever had a class that proves pundits are not any more accurate forecasters than the Silver's gone 99 for on predicting the state winners of the last two presidential elections. Nevertheless, I must have thought it sounded interesting and placed a hold on it at the library. We make approximations and assumptions about the world that are much cruder than we realize. However, he purposefully leaves out the mathematics. The end conclusion two streams - indexed investment on signal trading and short trading on the noise , I agree with. Data-driven predictions can succeed — and they can fail. The Signal and the Noise: Why So Many Predictions Fail--But Some Dont Reviews

Silver frames issues in terms of Bayes Theorem, which is a centuries-old mathematical formula for determining how probabilities change as new information becomes available. Climate scientist Michael E. He continues various areas in turn - all of which have their own forecasting issues, which are often very different leading to his third point the difficulty of drawing hard and fast rules around prediction. This is a fantastic book about predictions. If you've read Michael Lewis's The Big Short and Moneyball you can skip chapters 1 and 3 and if you've ever had a class that proves pundits are not any more accurate forecasters than the Silver's gone 99 for on predicting the state winners of the last two presidential elections. This book is entertaining as well as informative. Then I'm jarred out of complacency by a sudden shot from nowhere, in which he says that David Hume, one of the greatest philosophers of the 18th century, is simply too 'daft to understand' probabilistic arguments. I'd still recommend it to anyone with a love of charts, a thirst for interesting data-driven nonfiction, or anyone looking for something to shake up their reading list with something a little different. However, the quote is from the penultimate chapter, and there is no further insight inside this book. Yet, as frustrating as that may be, erring on the side caution, still might be a good thing, and remember, many weather forecasters, those working behind the scenes, are not being paid exorbitant fees. Welcome back. Original Title. Again, this was the unanimous opinion among my group. As for the content, I think that the idea of Baysean thinking is interesting and sound. It also has the most intelligent discussion of global warming that I have encountered. In addition to his own examples, he uses the classic example of how the rate of false positives in a sample of mammograms affects the actual probability that a positive test accurately predicts the presence of cancer. The Signal and the Noise print edition was named Amazon's No. Very highly recommended. Weather forecasts issued by the National Weather Service are unbiased in a probabilistic sense. Silve I finished this right before the elections, and I should have written my review before then so that I could convince more people to read this book when Nate Silver got more than Internet famous for a few weeks. The end. Nothing is more common than for someone like Silver--a media phenom with a strong platform his blog to phone a book in to cash in on his 15 minutes. So, overall, I really liked some parts. The difficulty in handling large amounts of data is separating the signal from the noise. A lot of survey nonfiction like this can be saved with interesting collateral content. These include the housing bubble, the collapse of the Soviet Union, and the Fukushima disaster. And are their forecasts really right? In other cases, prediction is still a very rudimentary-and dangerous-science. We imbue them with meaning. The problem then becomes how to quantify qualitative data. I am sure the vast majority of readers will roll a bemused eye at my anger over trivial details like this - but not only does it show that Silver very often doesn't take the time to understand his sources see Michael Mann's critique of Silver's presentation of global warming , but Silver's casual remarks could easily turn a lot of readers off to Hume before they've even read him. He explains and evaluates how these forecasters think and what bonds they share. I wanted to like this book as I enjoy reading Silver's blog. It's a technique for modulating new data to align its importance with older data. Silver does speak to political predictions. More Details It concluded that most of these findings were likely to fail when applied in the real world. But, there has to be an honesty in forecasting, too. Third, the models are constantly being improved as new data either affirms or disproves the latest prediction. But after opening the door, it The book has been published in eight languages. This was a fun read that tickled the nonfiction part of my brain in pleasant ways. For terrorist attacks he discussed power laws to extrapolate to major attacks which actually dominate costs and deaths and the importance of lateral and imaginative thinking around threats.

The Signal and the Noise: Why So Many Predictions Fail--But Some Dont Read Online

There are plenty of footnotes relevant to the page This is a really amazing book - a must read for anyone who makes decisions or judgement calls. Where I believe he often errs is in not needing a source for a statement that is pretty non-controversial; in these cases the comment could just be inserted into the text and the footnote dispensed with. And I just love this footnote, A conspiracy theory might be thought of as the laziest form of signal analysis. But what I was not expecting was the exten I picked up The Signal and the Noise from the library because I thought it would be slightly boring. It was really interesting coming to this book soon after reading The Black Swan, as in some ways they cover similar ground — but take a very different approach. Silver is a proponent of thinking probabilistically, which means making predictions and decisions based on the most likely outcome, given the data you have. I enjoyed the book very much and encourage you to read it! Many times, forecasters get things right, and many lives are saved, but at times, they get in right, but things are not as bad as predicted, such as the recent blizzard expected to hit NYC. The book focuses on predictions in a wide variety of topics; economics, the stock market, politics, baseball, basketball, weather, climate, earthquakes, chess, epidemics, poker, and terrorism! This is often called the "prior": how likely did you think it was that the woman had cancer before you saw the mammogram 2. I would have probably forgotten about it if it had been every once in a while, but geez! His writing style is casual, more impressive considering the subject material. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Please note that the tricks or techniques listed in this pdf are either fictional or claimed to work by its creator. It does the former, but not he latter. Without any introduction to the subject, he claims Hume is stuck in some 'skeptical shell' that prevents him from understanding the simple, elegant solutions of Bayes. It was enjoyable, certainly, and I learned a few things. Quotes from The Signal and th Each topic is covered lucidly, in sufficient detail, so that the reader gets a good grasp of the problems and issues for predictions. More importantly, he describes why methods that proved successful in one domain are inadequate or inappropriate to another domain. A final note: Silver is not the best writer; his prose is uneven and occasionally downright awkward. And then there's his problem with the word "literally. Silver seemed to quickly find his comfort level in treating one area after another in which we attempt to make predictions, with varying success. From such information, it's up to the consumer of such statements to use that information as best they can in dealing with an uncertain future in an age of information overload. Silver also discusses a technique called agent-based modeling, used to predict the spread of epidemics. Here he goes something like 7 for 13, very good in parts, solid in some, and misfires in others. Who could have predicted that from America's most famous stat-geek? The book is also well cited, which helps give weight to some of the more counterintuitive claims. I wish this were the core of the book. Silver is the founder and editor in chief of FiveThirtyEight. However, after reading this book, I think I will keep a closer eye on his website. In the United States presidential election between Barack Obama and Mitt Romney, he correctly predicted the winner of all 50 states and the District of Columbia. It's a technique for modulating new data to align its importance with older data. We make approximations and assumptions about the world that are much cruder than we realize. I really enjoyed the book, Nate's talk, and meeting him in person. Another classic on statistics. Predictions work best when they are 1 probabilistic i. I will first, however, describe what I thought is good about the book. The end conclusion two streams - indexed investment on signal trading and short trading on the noise , I agree with. Likewise, it may be possible to forecast terrorism, because that too, follows a power law! You don't have to spend energy paying attention to which station it is on and who he is catering to.

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