课程信息
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Learner Career Outcomes

21%

完成这些课程后已开始新的职业生涯

15%

通过此课程获得实实在在的工作福利

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

完成时间大约为22 小时

建议:Four weeks of study, two-five hours/week depending on your familiarity with mathematical statistics....

英语(English)

字幕:英语(English)

您将获得的技能

StatisticsBayesian StatisticsBayesian InferenceR Programming

Learner Career Outcomes

21%

完成这些课程后已开始新的职业生涯

15%

通过此课程获得实实在在的工作福利

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

完成时间大约为22 小时

建议:Four weeks of study, two-five hours/week depending on your familiarity with mathematical statistics....

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 3 小时

Probability and Bayes' Theorem

8 个视频 (总计 38 分钟), 4 个阅读材料, 5 个测验
8 个视频
Lesson 1.1 Classical and frequentist probability6分钟
Lesson 1.2 Bayesian probability and coherence3分钟
Lesson 2.1 Conditional probability4分钟
Lesson 2.2 Bayes' theorem6分钟
Lesson 3.1 Bernoulli and binomial distributions5分钟
Lesson 3.2 Uniform distribution5分钟
Lesson 3.3 Exponential and normal distributions2分钟
4 个阅读材料
Module 1 objectives, assignments, and supplementary materials3分钟
Background for Lesson 110分钟
Supplementary material for Lesson 23分钟
Supplementary material for Lesson 320分钟
5 个练习
Lesson 116分钟
Lesson 212分钟
Lesson 3.120分钟
Lesson 3.2-3.310分钟
Module 1 Honors15分钟
2
完成时间为 3 小时

Statistical Inference

11 个视频 (总计 59 分钟), 5 个阅读材料, 4 个测验
11 个视频
Lesson 4.2 Likelihood function and maximum likelihood7分钟
Lesson 4.3 Computing the MLE3分钟
Lesson 4.4 Computing the MLE: examples4分钟
Introduction to R6分钟
Plotting the likelihood in R4分钟
Plotting the likelihood in Excel4分钟
Lesson 5.1 Inference example: frequentist4分钟
Lesson 5.2 Inference example: Bayesian6分钟
Lesson 5.3 Continuous version of Bayes' theorem4分钟
Lesson 5.4 Posterior intervals7分钟
5 个阅读材料
Module 2 objectives, assignments, and supplementary materials3分钟
Background for Lesson 410分钟
Supplementary material for Lesson 45分钟
Background for Lesson 510分钟
Supplementary material for Lesson 510分钟
4 个练习
Lesson 48分钟
Lesson 5.1-5.218分钟
Lesson 5.3-5.416分钟
Module 2 Honors6分钟
3
完成时间为 2 小时

Priors and Models for Discrete Data

9 个视频 (总计 66 分钟), 2 个阅读材料, 4 个测验
9 个视频
Lesson 6.2 Prior predictive: binomial example5分钟
Lesson 6.3 Posterior predictive distribution4分钟
Lesson 7.1 Bernoulli/binomial likelihood with uniform prior3分钟
Lesson 7.2 Conjugate priors4分钟
Lesson 7.3 Posterior mean and effective sample size7分钟
Data analysis example in R12分钟
Data analysis example in Excel16分钟
Lesson 8.1 Poisson data8分钟
2 个阅读材料
Module 3 objectives, assignments, and supplementary materials3分钟
R and Excel code from example analysis10分钟
4 个练习
Lesson 612分钟
Lesson 715分钟
Lesson 815分钟
Module 3 Honors8分钟
4
完成时间为 3 小时

Models for Continuous Data

9 个视频 (总计 69 分钟), 5 个阅读材料, 5 个测验
9 个视频
Lesson 10.1 Normal likelihood with variance known3分钟
Lesson 10.2 Normal likelihood with variance unknown3分钟
Lesson 11.1 Non-informative priors8分钟
Lesson 11.2 Jeffreys prior3分钟
Linear regression in R17分钟
Linear regression in Excel (Analysis ToolPak)13分钟
Linear regression in Excel (StatPlus by AnalystSoft)14分钟
Conclusion1分钟
5 个阅读材料
Module 4 objectives, assignments, and supplementary materials3分钟
Supplementary material for Lesson 1010分钟
Supplementary material for Lesson 115分钟
Background for Lesson 1210分钟
R and Excel code for regression5分钟
5 个练习
Lesson 912分钟
Lesson 1020分钟
Lesson 1110分钟
Regression15分钟
Module 4 Honors6分钟
4.6
468 条评论Chevron Right

来自Bayesian Statistics: From Concept to Data Analysis的热门评论

创建者 GSSep 1st 2017

Good intro to Bayesian Statistics. Covers the basic concepts. Workload is reasonable and quizzes/exercises are helpful. Could include more exercises and additional backgroung/future reading materials.

创建者 JHJun 27th 2018

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.

讲师

Avatar

Herbert Lee

Professor
Applied Mathematics and Statistics

关于 加州大学圣克鲁兹分校

UC Santa Cruz is an outstanding public research university with a deep commitment to undergraduate education. It’s a place that connects people and programs in unexpected ways while providing unparalleled opportunities for students to learn through hands-on experience....

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  • 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.

  • 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.

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