# Reading 9: Bayesian Analysis#

*For the class on Monday, February 12th*

## Reading assignments#

Read the following sections of [ICVG20]:

Sec. 5.1 “Introduction to the Bayesian Method”

Lead paragraphs (text between the headings of Sec. 5.1 and Sec. 5.1.1)

All of its subsections, 5.1.1–5.1.3

Sec. 5.2 “Bayesian Priors”

Lead paragraphs (text between the headings of Sec. 5.2 and Sec. 5.2.1)

Subsection, 5.2.1 “Priors Assigned by Formal Rules”

Sec. 5.3. “Bayesian Parameter Uncertainty Quantification”

Lead paragraphs (text between the headings of Sec. 5.3 and Sec. 5.3.1)

And all of its subsections, 5.3.1–5.3.2

## Questions#

Hint

Submit your answer on Canvas. Due at noon, Monday, February 12th.

List anything from your reading that confuses you. Explain why they confuse you. If nothing confuses you, briefly summarize what you have learned from this reading assignment.

Use your own words to briefly explain (around or less than 100 words) the difference between Bayesian analysis and Maximum Likelihood Estimation.

## Discussion Preview#

Note

We will discuss the following questions in class. They are included here so that you have a chance to think about them before class.
You need *not* submit your answers as part of this assignment.

We will revisit some cases that we discussed in Classical Inference (Readings 3 and 4) and redo the analysis with the Bayesian approach.