Summary
To summarize this chapter, we discussed elements of probability theory; particularly those aspects required for learning Bayesian inference. Due to lack of space, we have not covered many elementary aspects of this subject. There are some excellent books on this subject, for example, books by William Feller (reference 2 in the References section of this chapter), E. T. Jaynes (reference 3 in the References section of this chapter), and M. Radziwill (reference 4 in the References section of this chapter). Readers are encouraged to read these to get a more in-depth understanding of probability theory and how it can be applied in real-life situations.
In the next chapter, we will introduce the R programming language that is the most popular ...
Get Learning Bayesian Models with R now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
