In this lesson, you will learn how to apply Bayesian Statistical Analysis to a data set, which you will do by following along in Chapter 1 of the free Book Probabilistic Programming and Bayesian Methods for Hackers. First, you will have a quick review of Bayesian statistics. Second, you will apply Bayesian statistics to a few simple examples. Next you will review several probability distributions. Finally, you will learn about monte-carol Markov Chains (MCMC), and how to use PyMC to sample from the posterior to estimate model parameters.
###Objectives ### By the end of this lesson, you will be able to:
- Understand the relevance of Bayesian statistics to the construction of models.
- Understand how to use PyMC to sample from the posterior.
- Understand how to use MCMC to determine model parameters and confidence intervals.
Approximately 3 hours.
- Python Statistical Exploration
- Another viewpoint on Statistical Analysis
When you have completed and worked through the above readings, please take the Week 12 Lesson 3 Assessment.