STT465: Bayesian Statistical Methods (MSU)

• Instructor: Gustavo de los Campos ( [email protected] )
• Time/Place: MW 10:20am-11:40am A120 Wells Hall (WH)
• Syllabus
• Required textbook
• R-software

In this course we follow closely the required textbook: "A first Course in Bayesian Statistical Methods" (P.D. Hoff).

HW

• Homework 1
• Homework 2
  • Gout data
• Homework 3
• Homework 4

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Lectures

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Chapter 1: Introduction and examples

• Lecture

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Chapter 2: Belief, probability and exchangeability

• Lecture

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Chapter 3: One-parameter models

• Lecture

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Chapter 4: Monte Carlo approximations

• Lecture
• Examples

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Chapter 5: The normal model

• Lecture
• Examples

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Chapter 6: Posterior approximation with the Gibbs sampler

• Lecture
• Examples

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Review of Linear Algebra & Multivariate Normal

• Lecture

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Multiple linear regression OLS and Maximum Likelihood

• Lecture Notes

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The multivariate normal distribution & intro to Bayesian multiple linear regression

• Slides set 1
• Slides set 2

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Chapter 10: Nonconjugate priors and Metropolis-Hastings algorithms

• Lecture
• Examples

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Chapter 11: Linear and generalized linear mixed effects models

• Lecture
• Examples

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Chapter 12: Latent variable methods for ordinal data

• Lecture
• Examples