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."
Этот курс входит в специализацию ''Специализация Statistics with R'
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Об этом курсе
3.9
Оценки: 504
Приобретаемые навыки
Bayesian StatisticsBayesian Linear RegressionBayesian InferenceR Programming
Программа курса: что вы изучите
Неделя
11 ч. на завершение
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 ((всего 2 мин.)), 4 материалов для самостоятельного изучения
1 видео
4 материала для самостоятельного изучения
About Statistics with R Specialization10мин
About Bayesian Statistics10мин
Pre-requisite Knowledge10мин
Special Thanks2мин
6 ч. на завершение
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 ((всего 41 мин.)), 2 материалов для самостоятельного изучения, 3 тестов
9 видео
Conditional Probabilities and Bayes' Rule2мин
Bayes' Rule and Diagnostic Testing6мин
Bayes Updating2мин
Bayesian vs. frequentist definitions of probability4мин
Inference for a Proportion: Frequentist Approach3мин
Inference for a Proportion: Bayesian Approach7мин
Effect of Sample Size on the Posterior2мин
Frequentist vs. Bayesian Inference9мин
2 материала для самостоятельного изучения
Module Learning Objectives
Week 1 Lab Instructions
3 практического упражнения
Week 1 Lab12мин
Week 1 Practice Quiz20мин
Week 1 Quiz20мин
Неделя
27 ч. на завершение
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 ((всего 45 мин.)), 2 материалов для самостоятельного изучения, 3 тестов
10 видео
From the Discrete to the Continuous5мин
Elicitation6мин
Conjugacy4мин
Inference on a Binomial Proportion5мин
The Gamma-Poisson Conjugate Families6мин
The Normal-Normal Conjugate Families3мин
Non-Conjugate Priors4мин
Credible Intervals3мин
Predictive Inference4мин
2 материала для самостоятельного изучения
Module Learning Objectives
Week 2 Lab Instructions
3 практического упражнения
Week 2 Lab28мин
Week 2 Practice Quiz20мин
Week 2 Quiz40мин
Неделя
38 ч. на завершение
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.
14 видео ((всего 75 мин.)), 2 материалов для самостоятельного изучения, 3 тестов
14 видео
Losses and decision making3мин
Working with loss functions6мин
Minimizing expected loss for hypothesis testing5мин
Posterior probabilities of hypotheses and Bayes factors6мин
The Normal-Gamma Conjugate Family6мин
Inference via Monte Carlo Sampling3мин
Predictive Distributions and Prior Choice5мин
Reference Priors7мин
Mixtures of Conjugate Priors and MCMC6мин
Hypothesis Testing: Normal Mean with Known Variance7мин
Comparing Two Paired Means Using Bayes' Factors6мин
Comparing Two Independent Means: Hypothesis Testing3мин
Comparing Two Independent Means: What to Report?5мин
2 материала для самостоятельного изучения
Module Learning Objectives
Week 3 Lab Instructions
3 практического упражнения
Week 3 Lab22мин
Week 3 Practice Quiz16мин
Week 3 Quiz40мин
Неделя
48 ч. на завершение
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 ((всего 72 мин.)), 2 материалов для самостоятельного изучения, 3 тестов
11 видео
Bayesian simple linear regression8мин
Checking for outliers4мин
Bayesian multiple regression4мин
Model selection criteria5мин
Bayesian model uncertainty7мин
Bayesian model averaging7мин
Stochastic exploration8мин
Priors for Bayesian model uncertainty8мин
R demo: crime and punishment9мин
Decisions under model uncertainty7мин
2 материала для самостоятельного изучения
Module Learning Objectives
Week 4 Lab Instructions
3 практического упражнения
Week 4 Lab22мин
Week 4 Practice Quiz20мин
Week 4 Quiz40мин
25%
начал новую карьеру, пройдя эти курсы
16%
получил значимые преимущества в карьере благодаря этому курсу
Лучшие рецензии
автор: RR•Sep 21st 2017
Great course. Difficult to apprehend sometimes as the Frequentist paradigm is learned first but once you get it, it is really amazing to see the believe update in action with data.
автор: GH•Apr 10th 2018
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.
Преподавателя
Mine Çetinkaya-Rundel
Associate Professor of the PracticeDepartment of Statistical Science
David Banks
Professor of the PracticeStatistical Science
Colin Rundel
Assistant Professor of the PracticeStatistical Science
Merlise A Clyde
ProfessorDepartment of Statistical Science
О 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.
О специализации ''Statistics with R'
In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.
You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.
Часто задаваемые вопросы
Когда я получу доступ к лекциям и заданиям?
Зарегистрировавшись на сертификацию, вы получите доступ ко всем видео, тестам и заданиям по программированию (если они предусмотрены). Задания по взаимной оценке сокурсниками можно сдавать и проверять только после начала сессии. Если вы проходите курс без оплаты, некоторые задания могут быть недоступны.
Что я получу, оформив подписку на специализацию?
Записавшись на курс, вы получите доступ ко всем курсам в специализации, а также возможность получить сертификат о его прохождении. После успешного прохождения курса на странице ваших достижений появится электронный сертификат. Оттуда его можно распечатать или прикрепить к профилю LinkedIn. Просто ознакомиться с содержанием курса можно бесплатно.
Какие правила возврата средств?
Можно ли получить финансовую помощь?
What background knowledge is necessary?
We assume you have knowledge equivalent to the prior courses in this specialization.
Will I receive a transcript from Duke University for completing this course?
No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
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