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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Introduction & Probability Review</title>
<meta charset="utf-8" />
<meta name="author" content="Stat 340: Bayesian Statistics" />
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class: title-slide, left, middle
# Introduction & Probability Review
### Stat 340: Bayesian Statistics
---
class: inverse, middle
.big-text[Welcome back!]
---
class: middle, clear
# 1. Welcome
# 2. Probability review
# 3. Logistics
# 4. Comparing the philosophies
---
class: middle, clear
.Large[
**Reading:**
Sections 7.1 - 7.2
<br>
**Homework for Friday:**
Review probability as needed
You should know how to solve the review problems
]
---
background-image: url("img/grand_marais.jpeg")
background-position: right
background-size: 50%
## About me
.pull-left[
- 5th year at Carleton
- Taught in Wisconsin for 4 years
- Ph.D., M.S. from Iowa State
- B.A. from Luther College
- Originally from Maryland
- Pragmatist (not a rabid Bayesian)
]
---
background-image: url(img/elephant.jpg)
background-size: cover
.footnote[.conte[Image from [Unsplash](https://unsplash.com)]]
---
## COVID-19 related policies
To ensure the well-being of each other and the broader community, we will
- stay home when sick. (Even if you don’t have COVID-19, you should stay home if you aren’t feeling well.)
- follow [CDC guidance](https://www.cdc.gov/coronavirus/2019-ncov/vaccines/fully-vaccinated.html) on testing, quarantine, and isolation.
- follow the College mask-wearing policy.
- not eat in class while the college is requiring mask wearing.
---
## What if Adam needs to miss class?
- I'll send a note through both email and Slack
- In most cases, class will still be held, but in a synchronous online format using Zoom
---
## What if I need to miss class?
- Let me know
- Check for materials (slide, handouts, etc.), assignments, and announcements on the course webpage
- Check out the collaborative notes for that day
- Stop by virtual student hours or make an appointment to ask questions
---
## Collaborative notes
- Each day, 2-3 of you will collaborate on notes to share with the class
- Creates a crowd-sourced version of what we do in class
- Helps anyone who needs to miss class
- You'll do this 2x throughout the course
- [Sign up here](https://docs.google.com/document/d/1-RjAbLxBAxEuy0R4HrMSHMwoNs0XilKwwT5cGBGswvM/edit?usp=sharing) (link is also on Week 01 webpage)
- Notes are due 24 hours after class
- Together, your 2 contributions count as a HW assignment
---
class: middle, inverse
.big-text[Probability review]
---
# Main results to review
.Large[
Be sure that you are comfortable with the following concepts/results from probability:
- Probability mass/density functions
- Joint probability/distributions
- Marginal probability/distributions
- **Conditional probability/distributions**
- **Bayes' rule**
- **Law of total probability**
]
---
# Example
.large[
A spam filter is designed by looking at commonly occurring phrases in spam. Suppose that 80% of email is spam. In 10% of the spam emails, the phrase “free money” is used, whereas this phrase is only used in 1% of non-spam emails.
1. What is the probability an email contains the phrase “free money” and is spam?
2. Write the event that an email contains the phrase “free money” as the union of disjoint events.
3. What is the probability that an email contains the phrase “free money?”
4. A new email has just arrived, which does mention “free money.” What is the probability that it is spam?
]
---
# Need more review?
.Large[
- Chapters 1-6 of [*Probability and Bayesian Modeling*](https://bayesball.github.io/BOOK/)
- [Section 2.1](https://ebookcentral.proquest.com/lib/carleton-ebooks/reader.action?docID=1648259&ppg=32) of *Bayesian Ideas and Data Analysis*
- My [probability lectures from fall 2020](https://carleton.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=2088b73f-581c-40e9-818f-ac23012a9103)
- [Stat 110 lectures](https://projects.iq.harvard.edu/stat110/youtube) by Joe Blitzstein at Harvard
]
---
class: inverse, middle
.big-text[Syllabus highlights]
## Please read the full syllabus
---
# Scheduled ~~office~~ student hours
.pull-left[
.center[**In person, CMC 307**]
.Large[
Day | Time
:----|:-----
Monday | 2:30-3:30
Tuesday | 12:30-1:30
Wednesday | 1-2
Thursday | 12:30-1:30
]
]
.pull-right[
.Large[
.center[**On Slack**]
Day | Time
:----|:-----
Monday | 8-9pm
Thursday | 8-9pm
]
]
<br>
.center[I'll also have appointment slots posted via Calendly]
---
# Types of "assignments"
.pull-left[
### Graded work
- Homework (10)
- Writing prompts (5)
- Problems (18 across 3 exams)
- Final project
]
.pull-right[
### Ungraded work
- Attendance and engagement
- Reading and review
]
---
background-image: url("figs/01-feedback_loop.png")
background-position: 50% 70%
background-size: 100%
# Learning takes place over time through feedback loops
---
class: highlight-last-item
<style type="text/css">
.highlight-last-item > ul > li,
.highlight-last-item > ol > li {
opacity: 0.5;
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opacity: 1;
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# Specifications grading
This courses uses a “specifications” (or “specs”) grading system
- Your work is evaluated on a pass/not yet basis, depending on whether or not it meets predefined specifications
--
- There are no points
--
- There is no partial credit
--
- Specs are clearly laid out and available from day 1
--
- There are opportunities to revise work that doesn't meet the specs
--
- Your grade is based on what you learn
---
class: highlight-last-item, incremental
# Perceived benefits
- It will be very clear which topics you need to work on
--
- Avoids one-and-done tests
--
- You decide which grade you're aiming for, and you know what you have to do to earn it
--
- Aligns your grade more directly with how much course material you have mastered, and in what depth
--
- Mirrors expectations in industry
---
class: highlight-last-item
# Possible drawbacks
- Unfamiliar system `\(\rightarrow\)` possible stress!
--
- Revisions take time
--
- If you have relied on partial credit `\(\rightarrow\)` shift mindset
---
# Baseline grades
.Large[
| Assignment/Grade | A | B | C | D |
|------------------|:-------:|:-------:|:------------:|:------------:|
| Homework (of 10) | 9 | 8 | 7 | 6 |
| Writing (of 5) | 5 | 4 | 3 | 2 |
| Problems (of 18) | 18 | 15 | 12 | 9 |
| Final project | Success | Success | Not required | Not required |
]
+/- based on your progress toward the next letter grade
Specifications are outlined on the [resources page](https://aloy.github.io/stat340/resources.html)
---
# Tokens
You can use a token to rewrite work that doesn't meet specifications
- Revision of a homework or writing assignment that did not earn a "Success"
- Revision of a writing assignment where you missed the rough draft or peer review (2 tokens, 1 for the revision, 1 for an exception to the intermediate deadlines)
- 48-hour extension on a homework assignment (the request must be submitted before the deadline)
- Revision of a Problem that earned a "Not yet"
---
# Tokens
- I will track token balances in the Moodle gradebook (updated weekly)
- You start the term with 4 tokens
- You can earn up to 3 more
---
# Why limit the number of tokens?
.large[
- You can't continually turn in substandard work without penalty
- Reflects reality in industry
- I am grading everything and you need prompt feedback
]
---
class: middle, inverse
.big-text[Questions?]
.Large[Please read the syllabus for many more details]
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