## Course Summary

**Bayesian Computational Analyses with R** is an introductory course on the use and implementation of Bayesian modeling using R software. The Bayesian approach is an alternative to the "frequentist" approach where one simply takes a sample of data and makes inferences

### Target Audience

- The course is ideal for anyone interested in learning both the conceptual and practical side of using Bayes' Rule to model likely event outcomes.
- The course is best suited for both students and professionals who currently make use of quantitative or probabilistic modeling.
- It is useful to have a working knowledge of either basic inferential statistics or probability theory.
- It is NOT necessary to have prior experience using R software to successfully complete and to benefit from this course.

### Pre-Requisites

- Students will need to install R and RStudio software, but ample instruction for doing so is provided in the course materials.

#### Curriculum

- Exercise Files
- Introduction to Bayesian Computational Analyses with R 02:00
- Introduction to Course Materials 02:19
- Introduction to R Software (slides, part 1) 09:54
- Introduction to R Software (slides, part 2) 08:43
- Introduction to R Software (slides, part 3) 12:16
- Introduction to R Software with Scripts (part 1) 07:25
- Introduction to R Software with Scripts (part 2) 09:52
- Introduction to R Software with Scripts (part 3) 11:50
- Introduction to R Software with Scripts (part 4) 08:38
- Introduction to R Software with Scripts (part 5) 07:40
- Programming a Monte Carlo Simulation 10:13
- Section 1 R Scripting Exercises 00:22

- Exercise Files
- More on the Course and Materials 02:19
- Session 1 R Scripting Exercise Solutions 12:32
- Background on Probability Density Functions (PDFs) 08:45
- Normal dnorm() Functions (part 1) 04:15
- Normal dnorm() Functions (part 2) 05:08
- Normal pnorm() Function 10:18
- Bayes' Rule and More 11:37
- Likelihood Function 06:58
- Using Discrete Priors (part 1) 08:32
- Using Discrete Priors (part 2) 06:34
- Using a Beta Prior (part 1) 05:12
- Using a Beta Prior (part 2) 07:10
- Using a Beta Prior (part 3) 05:05
- Simulating Beta Posteriors 04:38
- Brute Force Posterior Simulation using Histogram Prior 08:43
- Predictive Priors (slides) 06:24
- Predictive Priors (scripts, part 1) 07:23
- Predictive Priors (scripts, part 2) 07:19
- Section 2 Exercises 02:20
- Section 2 Exercise Solution 10:59

- Exercise Files
- Prelude to Single Parameter Models 11:17
- Single Parameter Models 10:59
- Heart Transplant Mortality Rate (part 1) 12:26
- Heart Transplant Mortality Rate (part 2) 10:40
- Test of Bayesian Robustness (part 1) 10:23
- Test of Bayesian Robustness (part 2) 11:02
- Exercise: How Many Taxis? 03:37
- Exercise Solution: How Many Taxis? 06:15

- Exercise Files
- Conjugate Mixtures (part 1) 10:37
- Conjugate Mixtures (part 2) 09:19
- A Bayesian Test of the Fairness of a Coin (part 1) 07:42
- A Bayesian Test of the Fairness of a Coin (part 2) 10:06
- More on the Fairness of a Coin (part 3) 11:30
- Introduction to Probability Density Functions (part 1) 13:26
- Intro to PDFs (part 2) 06:55
- Intro to PDFs (part 3) 07:14

- Exercise Files
- Mortality Rate Exercise Solution (part 1) 08:07
- Mortality Rate Exercise Solution (part 2) 07:53
- Normal Multiparameter Models (part 1) 11:03
- Normal Multiparameter Models (part 2) 08:21
- Normal Multiparameter Models (part 3) 07:59
- Multinomial Multiparameter Models (part 1) 10:23
- Multinomial Multiparameter Models (part 2) 11:34
- Bioassay Experiment (part 1) 10:32
- Bioassay Experiment (part 2) 10:47
- Exercise: Comparing Two Proportions 03:11
- Exercise Solution: Comparing Two Proportions (part 1) 05:13
- Exercise Solution: Comparing Two Proportions (part 2) 11:23

- Exercise Files
- Introduction to Bayesian Computation Section 06:08
- Computing Integrals to Estimate a Probability (part 1) 11:21
- Computing Integrals to Estimate a Probability (part 2) 10:20
- A Beta-Binomial Model of Overdispersion (part 1) 10:57
- A Beta-Binomial Model of Overdispersion (part 2) 10:50
- Exercise: Inference About a Normal Population 02:55
- Exercise Solution: Inference about a Normal Population 09:40

- Exercise Files
- Rejection Sampling (part 1) 10:07
- Rejection Sampling (part 2) 10:02
- Rejection Sampling (part 3) 06:42
- Rejection Sampling (part 4) 06:54
- Rejection Sampling (part 5) 10:40
- Rejection Sampling (part 6) 09:46
- Importance Sampling 08:26

- Exercise Files
- One-Sided Test of a Normal Mean (part 1) 09:51
- One-Sided Test of a Normal Mean (part 2) 09:41
- One-Sided Test of a Normal Mean (part 3) 07:44
- Two-Sided Test of a Normal Mean 11:33
- Streaky Behavior (part 1) 12:34
- Streaky Behavior (part 2) 10:26
- Streaky Behavior (part 3) 09:32
- Streaky Behavior (part 4) 08:44