Об этом курсе: This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm. The course will apply Bayesian methods to several practical problems, to show end-to-end Bayesian analyses that move from framing the question to building models to eliciting prior probabilities to implementing in R (free statistical software) the final posterior distribution. Additionally, the course will introduce credible regions, Bayesian comparisons of means and proportions, Bayesian regression and inference using multiple models, and discussion of Bayesian prediction. We assume learners in this course have background knowledge equivalent to what is covered in the earlier three courses in this specialization: "Introduction to Probability and Data," "Inferential Statistics," and "Linear Regression and Modeling."
Автор: Университет Дьюка
Преподаватели: Mine Çetinkaya-Rundel, Associate Professor of the Practice
Department of Statistical Science
Преподаватели: David Banks, Professor of the Practice
Statistical Science
Преподаватели: Colin Rundel , Assistant Professor of the Practice
Statistical Science
Преподаватели: Merlise A Clyde, Professor
Department of Statistical Science
| Основные сведения | Course 4 of 5 in the Statistics with R Specialization |
| Уровень | Intermediate |
| Выполнение | 5 weeks of study, 5-7 hours/week |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
About the Specialization and the Course
This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Bayesian Statistics. Please take several minutes read this information. Thanks for joining us in this course!
1 видео, 4 материалов для самостоятельного изучения
- Видео: Introduction to Statistics with R
- Reading: About Statistics with R Specialization
- Reading: About Bayesian Statistics
- Reading: Special Thanks
- Reading: Weekly Feedback Forms
- Discussion Prompt: Introduce Yourself
The Basics of Bayesian Statistics
<p>Welcome! Over the next several weeks, we will together explore Bayesian statistics. <p>In this module, we will work with conditional probabilities, which is the probability of event B given event A. Conditional probabilities are very important in medical decisions. By the end of the week, you will be able to solve problems using Bayes' rule, and update prior probabilities.</p><p>Please use the learning objectives and practice quiz to help you learn about Bayes' Rule, and apply what you have learned in the lab and on the quiz.
9 видео, 3 материалов для самостоятельного изучения, 1 practice quiz
- Видео: The Basics of Bayesian Statistics
- Reading: Module Learning Objectives
- Видео: Conditional Probabilities and Bayes' Rule
- Видео: Bayes' Rule and Diagnostic Testing
- Видео: Bayes Updating
- Видео: Bayesian vs. frequentist definitions of probability
- Видео: Inference for a Proportion: Frequentist Approach
- Видео: Inference for a Proportion: Bayesian Approach
- Видео: Effect of Sample Size on the Posterior
- Видео: Frequentist vs. Bayesian Inference
- Practice Quiz: Week 1 Practice Quiz
- Reading: Week 1 Lab Instructions
- Reading: Week 1 Feedback Form
Оцениваемый: Week 1 Quiz
Оцениваемый: Week 1 Lab
НЕДЕЛЯ 2
Bayesian Inference
In this week, we will discuss the continuous version of Bayes' rule and show you how to use it in a conjugate family, and discuss credible intervals. By the end of this week, you will be able to understand and define the concepts of prior, likelihood, and posterior probability and identify how they relate to one another.
10 видео, 3 материалов для самостоятельного изучения, 1 practice quiz
- Видео: Bayesian Inference
- Reading: Module Learning Objectives
- Видео: From the Discrete to the Continuous
- Видео: Elicitation
- Видео: Conjugacy
- Видео: Inference on a Binomial Proportion
- Видео: The Gamma-Poisson Conjugate Families
- Видео: The Normal-Normal Conjugate Families
- Видео: Non-Conjugate Priors
- Видео: Credible Intervals
- Видео: Predictive Inference
- Practice Quiz: Week 2 Practice Quiz
- Reading: Week 2 Lab Instructions
- Reading: Weekly Feedback Form
Оцениваемый: Week 2 Quiz
Оцениваемый: Week 2 Lab
НЕДЕЛЯ 3
Decision Making
In this module, we will discuss Bayesian decision making, hypothesis testing, and Bayesian testing. By the end of this week, you will be able to make optimal decisions based on Bayesian statistics and compare multiple hypotheses using Bayes Factors.
12 видео, 3 материалов для самостоятельного изучения, 1 practice quiz
- Видео: Decision making
- Reading: Module Learning Objectives
- Видео: Losses and decision making
- Видео: Working with loss functions
- Видео: Minimizing expected loss for hypothesis testing
- Видео: Posterior probabilities of hypotheses and Bayes factors
- Видео: Comparing two proportions using Bayes factors: assumptions
- Видео: Comparing two proportions using Bayes factors
- Видео: Comparing two paired means using Bayes factors
- Видео: What to report?
- Видео: Posterior probability, p-values and paradoxes
- Видео: Comparing two independent means
- Видео: Comparing two independent means: hypothesis testing
- Practice Quiz: Week 3 Practice Quiz
- Reading: Week 3 Lab Instructions
- Reading: Weekly Feedback Form
Оцениваемый: Week 3 Quiz
Оцениваемый: Week 3 Lab
НЕДЕЛЯ 4
Bayesian Regression
This week, we will look at Bayesian linear regressions and model averaging, which allows you to make inferences and predictions using several models. By the end of this week, you will be able to implement Bayesian model averaging, interpret Bayesian multiple linear regression and understand its relationship to the frequentist linear regression approach.
11 видео, 4 материалов для самостоятельного изучения, 1 practice quiz
- Видео: Bayesian regression
- Reading: Module Learning Objectives
- Видео: Bayesian simple linear regression
- Видео: Checking for outliers
- Видео: Bayesian multiple regression
- Видео: Model selection criteria
- Видео: Bayesian model uncertainty
- Видео: Bayesian model averaging
- Видео: Stochastic exploration
- Видео: Priors for Bayesian model uncertainty
- Видео: R demo: crime and punishment
- Видео: Decisions under model uncertainty
- Practice Quiz: Week 4 Practice Quiz
- Reading: Week 4 Lab Instructions
- Reading: Week 4 Lab Supplement
- Reading: Weekly Feedback Form
Оцениваемый: Week 4 Quiz
Оцениваемый: Week 4 Lab
НЕДЕЛЯ 5
Perspectives on Bayesian Applications
This week consists of interviews with statisticians on how they use Bayesian statistics in their work, as well as the final project in the course.
3 видео, 2 материалов для самостоятельного изучения
- Reading: About this module
- Видео: Bayesian inference: a talk with Jim Berger
- Видео: Bayesian methods and big data: a talk with David Dunson
- Видео: Bayesian methods in biostatistics and public health: a talk with Amy Herring
- Reading: Weekly Feedback Form
Data Analysis Project
In this module you will use the data set provided to complete and report on a data analysis question. Please read the background information, review the report template (downloaded from the link in Lesson Project Information), and then complete the peer review assignment.
2 материалов для самостоятельного изучения
- Reading: Project information
- Reading: Weekly Feedback Form
Оцениваемый: Data Analysis Project
Часто задаваемые вопросы
Как это работает
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Авторы
Университет Дьюка
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
Стоимость
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Рейтинги и отзывы
Strongly recommend!
Really hard, but absolutely worth it.
The instructors have great expertise, but this course is pretty difficult for a Bayesian newbie. Additional study guides would be helpful (especially week 4).
Coursera делает лучшее в мире образование доступным каждому, предлагая онлайн-курсы от ведущих университетов и организаций.
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I like this course a lot. Explanations are clear and much of the (unnecessarily heavyweight) maths is glossed over. I particularly liked the sections on Bayesian model selection.