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Applied Unsupervised Learning with R

You're reading from   Applied Unsupervised Learning with R Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA

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Product type Paperback
Published in Mar 2019
Publisher
ISBN-13 9781789956399
Length 320 pages
Edition 1st Edition
Languages
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Authors (2):
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Bradford Tuckfield Bradford Tuckfield
Author Profile Icon Bradford Tuckfield
Bradford Tuckfield
Alok Malik Alok Malik
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Alok Malik
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Basic Terminology of Probability Distributions


There are two families of methods in statistics: parametric and non-parametric methods. Non-parametric methods are meant to deal with data that could take any shape. Parametric methods, by contrast, make assumptions about the particular shape that data takes on. These assumptions are often encoded as parameters. The following are the two main parameters that you should be aware of:

  • Mean: This is the average of all values in the distribution.

  • Standard Deviation: This is the measure of the spread of values around the mean of a distribution.

Most of the parametric methods in statistics depend in some way on those two parameters. The parametric distributions that we're going to study in this chapter are these:

  • Uniform distributions

  • Normal distributions

  • Log-normal distributions.

  • Binomial distributions

  • Poisson distributions

  • Pareto distributions

Uniform Distribution

In the uniform distribution, all values between an interval, let's say [a,b], are equiprobable...

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