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The Signal and the Noise: Why So Many Predictions Fail – but Some Don't is a 2012 book by Nate Silver detailing the art of using probability and statistics as applied to real-world circumstances. The book includes case studies from baseball, elections, climate change, the 2008 financial crash, poker, and weather forecasting.
Intended to serve as a supplemental text for classes on probability theory and related topics, it covers cases where a mathematical proposition might seem to be true but actually turns out to be false. First published in 1987, the book received a second edition in 1997 and a third in 2013.
The Rademacher distribution, which takes value 1 with probability 1/2 and value −1 with probability 1/2. The binomial distribution, which describes the number of successes in a series of independent Yes/No experiments all with the same probability of success.
This page was last edited on 24 January 2019, at 22:44 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.
Probability and statistics are two closely related fields in mathematics that are sometimes combined for academic purposes. [1] They are covered in multiple articles and lists: Probability; Statistics; Glossary of probability and statistics; Notation in probability and statistics; Timeline of probability and statistics
Peter Gavin Hall AO FAA FRS [3] (20 November 1951 – 9 January 2016) was an Australian researcher in probability theory and mathematical statistics. [4] The American Statistical Association described him as one of the most influential and prolific theoretical statisticians in the history of the field. [5]
1654 – Blaise Pascal and Pierre de Fermat create the mathematical theory of probability, 1657 – Chistiaan Huygens's De ratiociniis in ludo aleae is the first book on mathematical probability, 1662 – John Graunt's Natural and Political Observations Made upon the Bills of Mortality makes inferences from statistical data on deaths in London,
This leads directly to the probability mass function of a Log(p)-distributed random variable: = for k ≥ 1, and where 0 < p < 1. Because of the identity above, the distribution is properly normalized. The cumulative distribution function is
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