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Julia for Data Science

You're reading from   Julia for Data Science high-performance computing simplified

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785289699
Length 346 pages
Edition 1st Edition
Languages
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Author (1):
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Anshul Joshi Anshul Joshi
Author Profile Icon Anshul Joshi
Anshul Joshi
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Table of Contents (12) Chapters Close

Preface 1. The Groundwork – Julia's Environment 2. Data Munging FREE CHAPTER 3. Data Exploration 4. Deep Dive into Inferential Statistics 5. Making Sense of Data Using Visualization 6. Supervised Machine Learning 7. Unsupervised Machine Learning 8. Creating Ensemble Models 9. Time Series 10. Collaborative Filtering and Recommendation System 11. Introduction to Deep Learning

Univariate distributions


The distributions where each sample is scalar are Univariate distributions. We can categorize them further into two distributions based on the values they support:

  • Univariate Continuous Distribution

  • Univariate Discrete Distribution

Abstract types:

julia> typealias UnivariateDistribution{S<:ValueSupport} Distribution{Univariate,S} 
 
julia> typealias DiscreteUnivariateDistribution   Distribution{Univariate, Discrete} 
julia> typealias ContinuousUnivariateDistribution Distribution{Univariate, Continuous} 

Many methods are implemented for Univariate distributions in the package, which provides necessary functionalities.

Retrieving parameters

  • params(distributionX): This will return a tuple of parameters

  • succprob(distributionX): This returns the probability of success

  • failprob(distributionX): This returns the probability of failure

  • dof(distributionX): This returns the degree of freedom

  • ncategories(distributionX): This returns the number of...

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