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#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 36 hours to complete

Suggested: 5 weeks of study, 4-6 hours/week....

#### English

Subtitles: English

### Skills you will gain

Gibbs SamplingBayesian StatisticsBayesian InferenceR Programming

#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 36 hours to complete

Suggested: 5 weeks of study, 4-6 hours/week....

#### English

Subtitles: English

### Syllabus - What you will learn from this course

Week
1
4 hours to complete

## Statistical modeling and Monte Carlo estimation

11 videos (Total 99 min), 4 readings, 4 quizzes
11 videos
Components of Bayesian models8m
Model specification7m
Posterior derivation9m
Non-conjugate models7m
Monte Carlo integration9m
Monte Carlo error and marginalization6m
Computing examples15m
Computing Monte Carlo error13m
Module 1 assignments and materials3m
Reference: Common probability distributions
Code for Lesson 3
Markov chains20m
4 practice exercises
Lesson 120m
Lesson 225m
Lesson 330m
Markov chains20m
Week
2
5 hours to complete

## Markov chain Monte Carlo (MCMC)

11 videos (Total 129 min), 7 readings, 4 quizzes
11 videos
Random walk example, Part 216m
Model writing, running, and post-processing12m
Multiple parameter sampling and full conditional distributions8m
Conditionally conjugate prior example with Normal likelihood10m
Computing example with Normal likelihood16m
Trace plots, autocorrelation17m
Multiple chains, burn-in, Gelman-Rubin diagnostic8m
Module 2 assignments and materials3m
Code for Lesson 4
Alternative MCMC software10m
Code from JAGS introduction
Code for Lesson 510m
Autocorrelation10m
Code for Lesson 6
4 practice exercises
Lesson 420m
Lesson 530m
Lesson 620m
MCMC45m
Week
3
6 hours to complete

## Common statistical models

11 videos (Total 131 min), 5 readings, 5 quizzes
11 videos
Model checking17m
Alternative models10m
Deviance information criterion (DIC)4m
Introduction to ANOVA10m
One way model using JAGS18m
Introduction to logistic regression6m
JAGS model (logistic regression)18m
Prediction15m
Module 3 assignments and materials3m
Code for Lesson 7
Code for Lesson 8
Code for Lesson 9
Multiple factor ANOVA20m
5 practice exercises
Lesson 7 Part A30m
Lesson 7 Part B30m
Lesson 830m
Lesson 945m
Common models and multiple factor ANOVA30m
Week
4
5 hours to complete

## Count data and hierarchical modeling

10 videos (Total 106 min), 7 readings, 4 quizzes
10 videos
Correlated data8m
Prior predictive simulation10m
JAGS model and model checking (hierarchical modeling)13m
Posterior predictive simulation8m
Linear regression example7m
Linear regression example in JAGS10m
Mixture model in JAGS13m
Module 4 assignments and materials3m
Prior sensitivity analysis20m
Code for Lesson 10
Normal hierarchical model20m
Applications of hierarchical modeling10m
Code and data for Lesson 11
Mixture model introduction, data, and code20m
4 practice exercises
Lesson 1040m
Lesson 11 Part A40m
Lesson 11 Part B30m
Predictive distributions and mixture models30m
4.8
61 Reviews

## 33%

started a new career after completing these courses

## 30%

got a tangible career benefit from this course

### Top reviews from Bayesian Statistics: Techniques and Models

By JHNov 1st 2017

This course is excellent! The material is very very interesting, the videos are of high quality and the quizzes and project really helps you getting it together. I really enjoyed it!!!

By MAAug 16th 2019

Very good courses. Maybe a little to slow at some moment (I not saying I understand better than other, I am talking about the rhytm). Otherwise perfect and very useful.

Doctoral Student
Statistics

## About University of California, Santa Cruz

UC Santa Cruz is an outstanding public research university with a deep commitment to undergraduate education. It’s a place that connects people and programs in unexpected ways while providing unparalleled opportunities for students to learn through hands-on experience....