課程信息
3.9
517 個評分
154 個審閱

第 4 門課程(共 5 門)

100% 在線

立即開始,按照自己的計劃學習。

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中級

完成時間大約為30 小時

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

英語(English)

字幕:英語(English)

您將獲得的技能

Bayesian StatisticsBayesian Linear RegressionBayesian InferenceR Programming

第 4 門課程(共 5 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為30 小時

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

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 1 小時

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 個視頻 (總計 2 分鐘), 4 個閱讀材料
1 個視頻
4 個閱讀材料
About Statistics with R Specialization10分鐘
About Bayesian Statistics10分鐘
Pre-requisite Knowledge10分鐘
Special Thanks2分鐘
完成時間為 6 小時

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 個視頻 (總計 41 分鐘), 2 個閱讀材料, 3 個測驗
9 個視頻
Conditional Probabilities and Bayes' Rule2分鐘
Bayes' Rule and Diagnostic Testing6分鐘
Bayes Updating2分鐘
Bayesian vs. frequentist definitions of probability4分鐘
Inference for a Proportion: Frequentist Approach3分鐘
Inference for a Proportion: Bayesian Approach7分鐘
Effect of Sample Size on the Posterior2分鐘
Frequentist vs. Bayesian Inference9分鐘
2 個閱讀材料
Module Learning Objectives
Week 1 Lab Instructions
3 個練習
Week 1 Lab12分鐘
Week 1 Practice Quiz20分鐘
Week 1 Quiz20分鐘
2
完成時間為 7 小時

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 個視頻 (總計 45 分鐘), 2 個閱讀材料, 3 個測驗
10 個視頻
From the Discrete to the Continuous5分鐘
Elicitation6分鐘
Conjugacy4分鐘
Inference on a Binomial Proportion5分鐘
The Gamma-Poisson Conjugate Families6分鐘
The Normal-Normal Conjugate Families3分鐘
Non-Conjugate Priors4分鐘
Credible Intervals3分鐘
Predictive Inference4分鐘
2 個閱讀材料
Module Learning Objectives
Week 2 Lab Instructions
3 個練習
Week 2 Lab28分鐘
Week 2 Practice Quiz20分鐘
Week 2 Quiz40分鐘
3
完成時間為 8 小時

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 個視頻 (總計 75 分鐘), 2 個閱讀材料, 3 個測驗
14 個視頻
Losses and decision making3分鐘
Working with loss functions6分鐘
Minimizing expected loss for hypothesis testing5分鐘
Posterior probabilities of hypotheses and Bayes factors6分鐘
The Normal-Gamma Conjugate Family6分鐘
Inference via Monte Carlo Sampling3分鐘
Predictive Distributions and Prior Choice5分鐘
Reference Priors7分鐘
Mixtures of Conjugate Priors and MCMC6分鐘
Hypothesis Testing: Normal Mean with Known Variance7分鐘
Comparing Two Paired Means Using Bayes' Factors6分鐘
Comparing Two Independent Means: Hypothesis Testing3分鐘
Comparing Two Independent Means: What to Report?5分鐘
2 個閱讀材料
Module Learning Objectives
Week 3 Lab Instructions
3 個練習
Week 3 Lab22分鐘
Week 3 Practice Quiz16分鐘
Week 3 Quiz40分鐘
4
完成時間為 8 小時

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 個視頻 (總計 72 分鐘), 2 個閱讀材料, 3 個測驗
11 個視頻
Bayesian simple linear regression8分鐘
Checking for outliers4分鐘
Bayesian multiple regression4分鐘
Model selection criteria5分鐘
Bayesian model uncertainty7分鐘
Bayesian model averaging7分鐘
Stochastic exploration8分鐘
Priors for Bayesian model uncertainty8分鐘
R demo: crime and punishment9分鐘
Decisions under model uncertainty7分鐘
2 個閱讀材料
Module Learning Objectives
Week 4 Lab Instructions
3 個練習
Week 4 Lab22分鐘
Week 4 Practice Quiz20分鐘
Week 4 Quiz40分鐘
3.9
154 個審閱Chevron Right

25%

完成這些課程後已開始新的職業生涯

16%

通過此課程獲得實實在在的工作福利

熱門審閱

創建者 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.

創建者 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.

講師

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Mine Çetinkaya-Rundel

Associate Professor of the Practice
Department of Statistical Science
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David Banks

Professor of the Practice
Statistical Science
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Colin Rundel

Assistant Professor of the Practice
Statistical Science
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Merlise A Clyde

Professor
Department of Statistical Science

關於 杜克大学

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....

關於 Statistics with R 專項課程

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

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  • 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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