June 2016
Beginner to intermediate
398 pages
8h 51m
English
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Summary
Generally, in science, every result should be reproducible, especially in quantitative analysis. This is possible by setting the seed of a deterministic pseudo random number generator. Secondly, and also very crucial, is to have a well-working random number generator to simulate random numbers from a uniform distribution. R's default random number generator, the Mersenne-Twister register-based algorithm, works pretty well. Simulated random numbers should possibly not be auto-correlated, and they should have a very long period. Otherwise, results might be biased and not trustable.
Based on uniform random numbers, random numbers from other distributions can be simulated. The important methods are the inversion method and rejection sampling. ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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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.