About this Course
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다음 전문 분야의 5개 강좌 중 4번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 30시간 필요

권장: 5 weeks of study, 5-7 hours/week...


자막: 영어

귀하가 습득할 기술

Bayesian StatisticsBayesian Linear RegressionBayesian InferenceR Programming

다음 전문 분야의 5개 강좌 중 4번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 30시간 필요

권장: 5 weeks of study, 5-7 hours/week...


자막: 영어

강의 계획 - 이 강좌에서 배울 내용

완료하는 데 1시간 필요

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 video (Total 2 min), 4 readings
1개의 동영상
4개의 읽기 자료
About Statistics with R Specialization10m
About Bayesian Statistics10m
Pre-requisite Knowledge10m
Special Thanks2m
완료하는 데 6시간 필요

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 videos (Total 41 min), 4 readings, 3 quizzes
9개의 동영상
Bayes Updating2m
Bayesian vs. frequentist definitions of probability4m
Inference for a Proportion: Frequentist Approach3m
Inference for a Proportion: Bayesian Approach7m
Effect of Sample Size on the Posterior2m
Frequentist vs. Bayesian Inference9m
4개의 읽기 자료
Module Learning Objectives2h
About Lab Choices10m
Week 1 Lab Instructions (RStudio)2h
Week 1 Lab Instructions (RStudio Cloud)10m
3개 연습문제
Week 1 Lab12m
Week 1 Practice Quiz20m
Week 1 Quiz20m
완료하는 데 7시간 필요

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 videos (Total 45 min), 3 readings, 3 quizzes
10개의 동영상
Inference on a Binomial Proportion5m
The Gamma-Poisson Conjugate Families6m
The Normal-Normal Conjugate Families3m
Non-Conjugate Priors4m
Credible Intervals3m
Predictive Inference4m
3개의 읽기 자료
Module Learning Objectives2h
Week 2 Lab Instructions (RStudio)3h
Week 1 Lab Instructions (RStudio Cloud)10m
3개 연습문제
Week 2 Lab28m
Week 2 Practice Quiz20m
Week 2 Quiz40m
완료하는 데 8시간 필요

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 videos (Total 75 min), 3 readings, 3 quizzes
14개의 동영상
Minimizing expected loss for hypothesis testing5m
Posterior probabilities of hypotheses and Bayes factors6m
The Normal-Gamma Conjugate Family6m
Inference via Monte Carlo Sampling3m
Predictive Distributions and Prior Choice5m
Reference Priors7m
Mixtures of Conjugate Priors and MCMC6m
Hypothesis Testing: Normal Mean with Known Variance7m
Comparing Two Paired Means Using Bayes' Factors6m
Comparing Two Independent Means: Hypothesis Testing3m
Comparing Two Independent Means: What to Report?5m
3개의 읽기 자료
Module Learning Objectives2h
Week 3 Lab Instructions (RStudio)3h
Week 3 Lab Instructions (RStudio Cloud)10m
3개 연습문제
Week 3 Lab22m
Week 3 Practice Quiz16m
Week 3 Quiz40m
완료하는 데 8시간 필요

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 videos (Total 72 min), 3 readings, 3 quizzes
11개의 동영상
Bayesian multiple regression4m
Model selection criteria5m
Bayesian model uncertainty7m
Bayesian model averaging7m
Stochastic exploration8m
Priors for Bayesian model uncertainty8m
R demo: crime and punishment9m
Decisions under model uncertainty7m
3개의 읽기 자료
Module Learning Objectives2h
Week 4 Lab Instructions (RStudio Cloud)3h
Week 4 Lab Instructions (RStudio Cloud)10m
3개 연습문제
Week 4 Lab22m
Week 4 Practice Quiz20m
Week 4 Quiz40m
158개의 리뷰Chevron Right


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베이지안 통계의 최상위 리뷰

대학: RRSep 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.

대학: GHApr 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.



Mine Çetinkaya-Rundel

Associate Professor of the Practice
Department of Statistical Science

David Banks

Professor of the Practice
Statistical Science

Colin Rundel

Assistant Professor of the Practice
Statistical Science

Merlise A Clyde

Department of Statistical Science

듀크대학교 정보

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....

Statistics with R 전문 분야 정보

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....
Statistics with R

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

  • We assume you have knowledge equivalent to the prior courses in this specialization.

  • 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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