本課程是贝叶斯统计專項課程專項課程的一部分

4.6
星
3,040 個評分

Herbert Lee

137,317 人已註冊

提供方

贝叶斯统计專項課程加州大学圣克鲁兹分校

課程信息

67,046 次近期查看

This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. For computing, you have the choice of using Microsoft Excel or the open-source, freely available statistical package R, with equivalent content for both options. The lectures provide some of the basic mathematical development as well as explanations of philosophy and interpretation. Completion of this course will give you an understanding of the concepts of the Bayesian approach, understanding the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses.

可靈活調整截止日期

根據您的日程表重置截止日期。

可分享的證書

完成後獲得證書

100% 在線

立即開始，按照自己的計劃學習。

第 1 門課程（共 5 門）

贝叶斯统计專項課程

中級

完成時間大約為12 小時

英語（English）

對員工進行熱門技能培訓能否為您的公司帶來益處？

體驗 Coursera 企業版

您將獲得的技能

Statistics
Bayesian Statistics
Bayesian Inference
R Programming

可靈活調整截止日期

根據您的日程表重置截止日期。

可分享的證書

完成後獲得證書

100% 在線

立即開始，按照自己的計劃學習。

第 1 門課程（共 5 門）

贝叶斯统计專項課程

中級

完成時間大約為12 小時

英語（English）

對員工進行熱門技能培訓能否為您的公司帶來益處？

體驗 Coursera 企業版

授課教師

授課教師評分

4.58/5 （455 個評分）

Herbert Lee

提供方

加州大学圣克鲁兹分校

了解頂級公司的員工如何掌握熱門技能

了解關於 Coursera for Business 的更多信息

授課大綱 - 您將從這門課程中學到什麼

內容評分91%（10,755 個評分）

週

週 1

完成時間為 3 小時

Probability and Bayes' Theorem

In this module, we review the basics of probability and Bayes’ theorem. In Lesson 1, we introduce the different paradigms or definitions of probability and discuss why probability provides a coherent framework for dealing with uncertainty. In Lesson 2, we review the rules of conditional probability and introduce Bayes’ theorem. Lesson 3 reviews common probability distributions for discrete and continuous random variables.

完成時間為 3 小時

8 個視頻（總計 38 分鐘）, 4 個閱讀材料, 5 個測驗

8 個視頻

Course introduction4分鐘

Lesson 1.1 Classical and frequentist probability6分鐘

Lesson 1.2 Bayesian probability and coherence3分鐘

Lesson 2.1 Conditional probability4分鐘

Lesson 2.2 Bayes' theorem6分鐘

Lesson 3.1 Bernoulli and binomial distributions5分鐘

Lesson 3.2 Uniform distribution5分鐘

Lesson 3.3 Exponential and normal distributions2分鐘

4 個閱讀材料

Module 1 objectives, assignments, and supplementary materials3分鐘

Background for Lesson 110分鐘

Supplementary material for Lesson 23分鐘

Supplementary material for Lesson 320分鐘

5 個練習

Lesson 1: Demonstrate your knowledge30分鐘

Lesson 2: Demonstrate your knowledge12分鐘

Lesson 3.1: Demonstrate your knowledge30分鐘

Lesson 3.2-3.3: Demonstrate your knowledge10分鐘

Module 1 Honors15分鐘

週

週 2

完成時間為 3 小時

Statistical Inference

This module introduces concepts of statistical inference from both frequentist and Bayesian perspectives. Lesson 4 takes the frequentist view, demonstrating maximum likelihood estimation and confidence intervals for binomial data. Lesson 5 introduces the fundamentals of Bayesian inference. Beginning with a binomial likelihood and prior probabilities for simple hypotheses, you will learn how to use Bayes’ theorem to update the prior with data to obtain posterior probabilities. This framework is extended with the continuous version of Bayes theorem to estimate continuous model parameters, and calculate posterior probabilities and credible intervals.

完成時間為 3 小時

11 個視頻（總計 59 分鐘）, 5 個閱讀材料, 4 個測驗

11 個視頻

Lesson 4.1 Confidence intervals4分鐘

Lesson 4.2 Likelihood function and maximum likelihood7分鐘

Lesson 4.3 Computing the MLE3分鐘

Lesson 4.4 Computing the MLE: examples4分鐘

Introduction to R6分鐘

Plotting the likelihood in R4分鐘

Plotting the likelihood in Excel4分鐘

Lesson 5.1 Inference example: frequentist4分鐘

Lesson 5.2 Inference example: Bayesian6分鐘

Lesson 5.3 Continuous version of Bayes' theorem4分鐘

Lesson 5.4 Posterior intervals7分鐘

5 個閱讀材料

Module 2 objectives, assignments, and supplementary materials3分鐘

Background for Lesson 410分鐘

Supplementary material for Lesson 45分鐘

Background for Lesson 510分鐘

Supplementary material for Lesson 510分鐘

4 個練習

Lesson 4: Demonstrate your knowledge8分鐘

Lesson 5.1-5.2: Demonstrate your knowledge30分鐘

Lesson 5.3-5.4: Demonstrate your knowledge30分鐘

Module 2 Honors6分鐘

週

週 3

完成時間為 3 小時

Priors and Models for Discrete Data

In this module, you will learn methods for selecting prior distributions and building models for discrete data. Lesson 6 introduces prior selection and predictive distributions as a means of evaluating priors. Lesson 7 demonstrates Bayesian analysis of Bernoulli data and introduces the computationally convenient concept of conjugate priors. Lesson 8 builds a conjugate model for Poisson data and discusses strategies for selection of prior hyperparameters.

完成時間為 3 小時

9 個視頻（總計 66 分鐘）, 2 個閱讀材料, 4 個測驗

9 個視頻

Lesson 6.1 Priors and prior predictive distributions3分鐘

Lesson 6.2 Prior predictive: binomial example5分鐘

Lesson 6.3 Posterior predictive distribution4分鐘

Lesson 7.1 Bernoulli/binomial likelihood with uniform prior3分鐘

Lesson 7.2 Conjugate priors4分鐘

Lesson 7.3 Posterior mean and effective sample size7分鐘

Data analysis example in R12分鐘

Data analysis example in Excel16分鐘

Lesson 8.1 Poisson data8分鐘

2 個閱讀材料

Module 3 objectives, assignments, and supplementary materials3分鐘

R and Excel code from example analysis10分鐘

4 個練習

Lesson 6: Demonstrate your knowledge30分鐘

Lesson 7: Demonstrate your knowledge15分鐘

Lesson 8: Demonstrate your knowledge15分鐘

Module 3 Honors8分鐘

週

週 4

完成時間為 3 小時

Models for Continuous Data

This module covers conjugate and objective Bayesian analysis for continuous data. Lesson 9 presents the conjugate model for exponentially distributed data. Lesson 10 discusses models for normally distributed data, which play a central role in statistics. In Lesson 11, we return to prior selection and discuss ‘objective’ or ‘non-informative’ priors. Lesson 12 presents Bayesian linear regression with non-informative priors, which yield results comparable to those of classical regression.

完成時間為 3 小時

9 個視頻（總計 69 分鐘）, 5 個閱讀材料, 5 個測驗

9 個視頻

Lesson 9.1 Exponential data4分鐘

Lesson 10.1 Normal likelihood with variance known3分鐘

Lesson 10.2 Normal likelihood with variance unknown3分鐘

Lesson 11.1 Non-informative priors8分鐘

Lesson 11.2 Jeffreys prior3分鐘

Linear regression in R17分鐘

Linear regression in Excel (Analysis ToolPak)13分鐘

Linear regression in Excel (StatPlus by AnalystSoft)14分鐘

Conclusion1分鐘

5 個閱讀材料

Module 4 objectives, assignments, and supplementary materials3分鐘

Supplementary material for Lesson 1010分鐘

Supplementary material for Lesson 115分鐘

Background for Lesson 1210分鐘

R and Excel code for regression5分鐘

5 個練習

Lesson 9: Demonstrate your knowledge12分鐘

Lesson 10: Demonstrate your knowledge20分鐘

Lesson 11: Demonstrate your knowledge10分鐘

Regression: Demonstrate your knowledge15分鐘

Module 4 Honors6分鐘

審閱

4.6

794 條評論

5 stars
66.95%
4 stars
25.45%
3 stars
5.29%
2 stars
1.34%
1 star
0.95%

來自BAYESIAN STATISTICS: FROM CONCEPT TO DATA ANALYSIS的熱門評論

由 MT 提供2017年7月18日

Exceptional course on probabilities and statistics from a Bayesian point of view. I would recommend this course to anyone wishing to learn more about probabilities and statistics.

由 JH 提供2018年6月26日

Great course. The content moves at a nice pace and the videos are really good to follow. The Quizzes are also set at a good level. You can't pass this course unless you have understood the material.

由 BU 提供2020年5月24日

This was an invaluable learning experience. I was delighted to go through this learning.

I strongly suggest this course to anyone who wants to learn or refresh their statistics knowledge.

由 AO 提供2020年7月20日

Prof. Lee's approach is simple and intuitive. There are exercises throughout the videos, which help you make sure you and the professor are on the same track. I would certainly recommend it!

查看所有評論

關於贝叶斯统计專項課程

This Specialization is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.

常見問題

我什么时候能够访问课程视频和作业？
讲座和作业的访问权限取决于您的注册类型。如果您以旁听模式参加课程，则可以免费查看大多数课程资料。要访问评分作业并获得证书，您需要在旁听期间或之后购买证书体验。如果看不到旁听选项：
课程可能不提供旁听选项。您可以尝试免费试用，也可以申请助学金。
课程可能会改为提供'完整课程，没有证书'。通过此选项，您可以查看所有课程材料、提交所要求的作业，以及获得最终成绩。这也意味着您将无法购买证书体验。
我订阅此专项课程后会得到什么？
您注册课程后，将有权访问专项课程中的所有课程，并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。
有助学金吗？
是的。如果您负担不起学习计划的注册费，则可以在选择学习计划时申请助学金或奖学金。如果您选择的学习计划提供助学金或奖学金，则您将在描述页面上看到一个申请链接。
What are the pre-requisites for this course?
You should have exposure to the concepts from a basic statistics class (for example, probability, the Central Limit Theorem, confidence intervals, linear regression) and calculus (integration and differentiation), but it is not expected that you remember how to do all of these items. The course will provide some overview of the statistical concepts, which should be enough to remind you of the necessary details if you've at least seen the concepts previously. On the calculus side, the lectures will include some use of calculus, so it is important that you understand the concept of an integral as finding the area under a curve, or differentiating to find a maximum, but you will not be required to do any integration or differentiation yourself.
What computing resources are expected for this course?
Data analysis is done using computer software. This course provides the option of Excel or R. Equivalent content is provided for both options. A very brief introduction to R is provided for people who have never used it before, but this is not meant to be a course on R. Learners using Excel are expected to already have basic familiarity of Excel.

還有其他問題嗎？請訪問學生幫助中心。

培養員工技能，推動業務成果

探索 Coursera for Business

Bayesian Statistics: From Concept to Data Analysis

課程信息

您將獲得的技能