The course schedule below will be filled in throughout the semester. For each day, there will be a link to course slides (if there are any that day) and notes/links on what is due ahead of class time. The “Announcements” column is (hopefully) self-explanatory. Any urgent announcements will be made over email.
1/20: Winter Break
Before the semester: (Optional!)
Read the course syllabus. I will assume that you understand all course policies beginning on Tuesday of next week! Familiarize yourself with Moodle and the course website Complete Introductions Survey Join Slack |
1/22: First Day of Class, Probability Review
Before class:
Read the course syllabus. I will assume that you understand all course policies beginning on Tuesday of next week! Familiarize yourself with Moodle and the course website Complete Introductions Survey Join Slack
During class:
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Welcome (back) to campus!
- PS1 due Tuesday 2/10 (posted by Tuesday 1/27)
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1/27: Frequentist Estimation
Before class:
Read through Section 1.3 of the Statistical Theory course notes, and make sure you’re familiar with all concepts other than Jacobians / Jacobian Matrices (we won’t use them in this course). Make sure you can answer the first two Concept Questions in Section 1.2 of the Statistical Theory course notes, and think about an answer to the third question. Make at least one slack post, either in the #discussion-boards channel related to the pre-class material, or in #random introducing yourself to people in class!
During class:
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1/29: Bayesian Estimation
Before class:
Read through the beginning of Chapter 2 of the Statistical Theory course notes (you can skip the section “When maximizing the ‘usual’ way doesn’t work…”) Finish working through the solution to the MLEs for \(\mu\) and \(\sigma^2\) in the Normal model (worked example 2, Section 2.5). Check your answer with the solutions in the Statistical Theory course notes Make at least one slack post in #general (responding to someone else, with your own post, etc.) regarding the material we’ve covered so far (prob review + MLEs + anything on PS1 you’ve looked at). As a reminder, this can be a comment, question, or something “digging deeper” (what are you still thinking about, can you draw connections to other classes/topics/things you’ve read, etc.)
During class:
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- PS1 due Tuesday 2/10
- Make an Overleaf account if you don’t already have one, and create a project for your 454 problem sets!
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2/3: Bayesian Estimation II (conjugate priors)
Before class:
Read through Section 1.1 and 1.2 of Bayes’ Rules for some background on Bayesian thinking Read through Sections 1.1 - 1.3 of Bayesian Theory for another perspective on the history of Bayesian philosophy Remember, you need to average 2 slack posts each week! Here are a few options:
What stuck out to you in the history of Bayesian statistics? Is it a philosophy you align with? Why or why not? For the coded Beta(1,1)-Binomial example we did in class on Thursday, mess around with different n’s and x’s (maybe same p every time, or maybe same x every time and changing p) and visualize how your posterior changes. Post a summary plot of your findings and some thoughts in the #coding channel! Post something you’re stuck on. Do you have questions on the practice problems given at the end of class? Do you think you have an algebra error somewhere but can’t find it? Your classmates and I can help you de-bug your math!
During class:
Beta-Binomial Poisson-Gamma
Code:
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2/5: Frequentist vs. Bayesian Estimators
Before class:
Read through Section 5.1 and 5.2 of Bayes’ Rules for some background on the conjugate priors we’ve covered so far Work through the algebraic details of the Normal-Normal conjugate model where the variance \((\sigma^2)\) is known (see equation 5.15 in Bayes’ Rules). Try it for yourself first, then check the solution in Section 5.3.4 of Bayes’ Rules. Slack post (as per usual). All the usual options apply! Here are three extra ones to post in #coding:
Plot the prior and posterior Beta distributions next to each other, like we’ve done before in class. Try changing prior parameters and likelihoods to see how things shift, and post your thoughts (and code) to the #coding channel Similar plotting question as above, but try it out with the Poisson-Gamma (this is a bit more challenging since I haven’t given you specific code for this one). If you are really code motivated, try writing a function to take in some prior parameters and data (likelihood) that produces a plot of the posterior, likelihood, and prior for a given conjugate family!
During class:
- Posterior means as weighted averages
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- PS1 due Tuesday 2/10
- Theory Exam: Tuesday 2/17
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