November 2016
Beginner to intermediate
282 pages
6h 58m
English
Content preview from Bayesian Analysis with Python
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Chapter 1. Thinking Probabilistically - A Bayesian Inference Primer
Probability theory is nothing but common sense reduced to calculation. | ||
| --Pierre-Simon Laplace | ||
In this chapter, we will learn the core concepts of Bayesian statistics and some of the instruments in the Bayesian toolbox. We will use some Python code in this chapter, but this chapter will be mostly theoretical; most of the concepts in this chapter will be revisited many times through the rest of the book. This chapter, being intense on the theoretical side, may be a little anxiogenic for the coder in you, but I think it will ease the path to effectively applying Bayesian statistics to your problems.
In this chapter, we will cover the following topics:
- Statistical modeling
- Probabilities ...
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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.