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- W4249885815 abstract "Probability provides the theoretical basis for almost all of machine learning and most of analytics, and it is a critical mindset for data scientists to be able to adopt. This chapter attempts to build up the subject of probability in a very intuitive way. It shows two of the simplest, most intuitive, and most important probability models, including coin flipping, and dart throwing. Using these as motivation, the chapter gives a more formal treatment of probability concepts. It focuses on several of the most important probability distributions for data scientist. A single-dimensional random variable (RV) is described by a probability mass function if it is discrete or a probability distribution function if it is continuous. We can also have a RV that returns a random vector of d-dimensions. The concepts of probability mass function and probability density generalize naturally." @default.
- W4249885815 created "2022-05-12" @default.
- W4249885815 date "2017-01-27" @default.
- W4249885815 modified "2023-09-27" @default.
- W4249885815 title "Probability" @default.
- W4249885815 doi "https://doi.org/10.1002/9781119092919.ch18" @default.
- W4249885815 hasPublicationYear "2017" @default.
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