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STA 610L: Module 1.1

Course overview

Dr. Olanrewaju Michael Akande

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Welcome to STA 610L!

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What is this course about?

Learn the foundations of hierarchical modeling.

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What is this course about?

Learn the foundations of hierarchical modeling.

Work through the theory of several hierarchical models.

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What is this course about?

Learn the foundations of hierarchical modeling.

Work through the theory of several hierarchical models.

Use hierarchical models to answer inferential questions.

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What is this course about?

Learn the foundations of hierarchical modeling.

Work through the theory of several hierarchical models.

Use hierarchical models to answer inferential questions.

Apply the models to real datasets.

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What is this course about?

Learn the foundations of hierarchical modeling.

Work through the theory of several hierarchical models.

Use hierarchical models to answer inferential questions.

Apply the models to real datasets.

Honing collaborative and presentations skills.

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What is this course about?

Learn the foundations of hierarchical modeling.

Work through the theory of several hierarchical models.

Use hierarchical models to answer inferential questions.

Apply the models to real datasets.

Honing collaborative and presentations skills.


The Bayesian paradigm is well suited for building hierarchical models. Usually you just have several levels of conditional distributions making up the prior.

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Instructor

Dr. Olanrewaju Michael Akande

  olanrewaju.akande@duke.edu
  akandelanre.github.io.
  See course website
  See course website

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TAs

Jiurui Tang

  jiurui.tang@duke.edu
  See course website
  See course website

Meng (Amy) Xie

  meng.xie@duke.edu
  See course website
  See course website

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FAQs

All materials and information will be posted on the course webpage:

https://sta-610l-s21.github.io/Course-Website/

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FAQs

All materials and information will be posted on the course webpage:

https://sta-610l-s21.github.io/Course-Website/

  • How much theory will this class cover? A good amount.
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FAQs

All materials and information will be posted on the course webpage:

https://sta-610l-s21.github.io/Course-Website/

  • How much theory will this class cover? A good amount.

  • Am I prepared to take this course? Yes, if you are familiar with the topics covered in STA 360/601/602 (Bayesian Inference) and all its prerequisite at Duke.

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FAQs

All materials and information will be posted on the course webpage:

https://sta-610l-s21.github.io/Course-Website/

  • How much theory will this class cover? A good amount.

  • Am I prepared to take this course? Yes, if you are familiar with the topics covered in STA 360/601/602 (Bayesian Inference) and all its prerequisite at Duke.

  • What if I can't remember the topics in the prerequisites? See the review materials in the next module.

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FAQs

All materials and information will be posted on the course webpage:

https://sta-610l-s21.github.io/Course-Website/

  • How much theory will this class cover? A good amount.

  • Am I prepared to take this course? Yes, if you are familiar with the topics covered in STA 360/601/602 (Bayesian Inference) and all its prerequisite at Duke.

  • What if I can't remember the topics in the prerequisites? See the review materials in the next module.

  • Will we be doing "very heavy" computing? A fair amount.

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FAQs

All materials and information will be posted on the course webpage:

https://sta-610l-s21.github.io/Course-Website/

  • How much theory will this class cover? A good amount.

  • Am I prepared to take this course? Yes, if you are familiar with the topics covered in STA 360/601/602 (Bayesian Inference) and all its prerequisite at Duke.

  • What if I can't remember the topics in the prerequisites? See the review materials in the next module.

  • Will we be doing "very heavy" computing? A fair amount.

  • What computing language will we use? R!

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FAQs

All materials and information will be posted on the course webpage:

https://sta-610l-s21.github.io/Course-Website/

  • How much theory will this class cover? A good amount.

  • Am I prepared to take this course? Yes, if you are familiar with the topics covered in STA 360/601/602 (Bayesian Inference) and all its prerequisite at Duke.

  • What if I can't remember the topics in the prerequisites? See the review materials in the next module.

  • Will we be doing "very heavy" computing? A fair amount.

  • What computing language will we use? R!

  • What if I don't know R? This course assumes you already know R but you can still learn on the fly (you must be self-motivated). Here are some resources for you: https://sta-610l-s21.github.io/Course-Website/resources/.

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Course structure and policies

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Course structure and policies

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Course structure and policies

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Course structure and policies

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Course structure and policies

  • See: https://sta-610l-s21.github.io/Course-Website/policies/

  • Make use of the teaching team's office hours, we're here to help!

  • Do not hesitate to come to my office hours or you can also make an appointment to discuss a homework problem or any aspect of the course.

  • When the teaching team has announcements for you we will send an email to your Duke email address. Please make sure to check your email daily.

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Course structure and policies

  • See: https://sta-610l-s21.github.io/Course-Website/policies/

  • Make use of the teaching team's office hours, we're here to help!

  • Do not hesitate to come to my office hours or you can also make an appointment to discuss a homework problem or any aspect of the course.

  • When the teaching team has announcements for you we will send an email to your Duke email address. Please make sure to check your email daily.

  • Try as much as possible to refrain from texting or using your computer for anything other than coursework while watching the lecture videos and during discussion sessions.

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Other details

  • What topics will we cover? Refer to Section 13 of the syllabus (here: Syllabus).
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Other details

  • What topics will we cover? Refer to Section 13 of the syllabus (here: Syllabus).

  • Also refer to the schedule on the website for updated breakdown of the courses. Remember to refresh the page frequently. See here: Class Schedule.

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Other details

  • What topics will we cover? Refer to Section 13 of the syllabus (here: Syllabus).

  • Also refer to the schedule on the website for updated breakdown of the courses. Remember to refresh the page frequently. See here: Class Schedule.

  • If you are auditing this course, remember to complete the necessary audit forms.

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Other details

  • What topics will we cover? Refer to Section 13 of the syllabus (here: Syllabus).

  • Also refer to the schedule on the website for updated breakdown of the courses. Remember to refresh the page frequently. See here: Class Schedule.

  • If you are auditing this course, remember to complete the necessary audit forms.

  • Confirm that you have access to Sakai, Piazza and Gradescope.

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What's next?

Move on to the readings for the next module!

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Welcome to STA 610L!

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