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Julia for Data Science

You're reading from  Julia for Data Science

Product type Book
Published in Sep 2016
Publisher Packt
ISBN-13 9781785289699
Pages 346 pages
Edition 1st Edition
Languages
Author (1):
Anshul Joshi Anshul Joshi
Profile icon Anshul Joshi

Table of Contents (17) Chapters

Julia for Data Science
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. The Groundwork – Julia's Environment 2. Data Munging 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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