September 2016
Beginner to intermediate
346 pages
6h 35m
English
Content preview from Julia for Data Science
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Type hierarchy in Distributions.jl
The functions provided in Distributions.jl follow a hierarchy. Let's go through it to understand the capabilities of the package.
Understanding Sampleable
Sampleable is an abstract type that includes samplers and distributions from which one can draw samples. It is defined as follows:
The kinds of samples that can be drawn are defined by the two parameter types:
- VariateForm:
- Univariate: Scalar number
- Multivariate: Numeric vector
- Matrixvariate: Numeric matrix
- ValueSupport:
- Discrete: Int
- Continuous: Float64
We can extract the information about the sample that the Sampleable object generates. An array can contain multiple ...
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.