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  3. Fundamentals of Bayesian Data Analysis in R
  • 1

    What is Bayesian Data Analysis?

    Free

    This chapter will introduce you to Bayesian data analysis and give you a feel for how it works.

  • 2

    How does Bayesian inference work?

    In this chapter we will take a detailed look at the foundations of Bayesian inference.

  • 3

    Why use Bayesian Data Analysis?

    This chapter will show you four reasons why Bayesian data analysis is a useful tool to have in your data science tool belt.

  • 4

    Bayesian inference with Bayes' theorem

    Learn what Bayes theorem is all about and how to use it for statistical inference.

  • 5

    More parameters, more data, and more Bayes

    Learn about using the Normal distribution to analyze continuous data and try out a tool for practical Bayesian analysis in R.

Initializing

Exercise

Summarizing the zombie drug experiment

The point of working with samples from a probability distribution is that it makes it easy to calculate new measures of interest. The following tasks are about doing just this!

Instructions 1/4

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A point estimate is a single number used to summarize what's known about a parameter of interest. It can be seen as a "best guess" of the value of the parameter. A commonly used point estimate is the median of the posterior. It's the midpoint of the distribution, and it's equally probable for the parameter value to be larger than the median as it is to be smaller than it.

  • Calculate the median of posterior using the median() function built into R.