January 2018
Beginner to intermediate
548 pages
12h 11m
English
Content preview from IPython Interactive Computing and Visualization Cookbook - Second EditionBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Estimating a probability distribution nonparametrically with a kernel density estimation
In the previous recipe, we applied a parametric estimation method. We had a statistical model (the exponential distribution) describing our data, and we estimated a single parameter (the rate of the distribution). Nonparametric estimation deals with statistical models that do not belong to a known family of distributions. The parameter space is then infinite-dimensional instead of finite-dimensional (that is, we estimate functions rather than numbers).
Here, we use a Kernel Density Estimation (KDE) to estimate the density of probability of a spatial distribution. We look at the geographical locations of tropical cyclones from 1848 to 2013, based on data provided ...
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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.