# 学生对 加州大学圣克鲁兹分校 提供的 Bayesian Statistics: From Concept to Data Analysis 的评价和反馈

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2,501 个评分
658 条评论

## 课程概述

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....

## 热门审阅

GS

Sep 01, 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.

JB

Oct 17, 2020

An excellent course with some good hands on exercises in both R and excel. Not for the faint of heart mathematically speaking, assumes a competent understanding of statistics and probability going in

## 151 - Bayesian Statistics: From Concept to Data Analysis 的 175 个评论（共 646 个）

May 07, 2019

simple, clear and enjoyable. will take the second course in the series, then move to heavy literature on the topic.

Special thank you to the instructor! you are amazing!

Jul 14, 2017

This is a good course. The instructor offers additional material that help with the understanding of the material, along with enough quizzes to help with practical use.

Mar 13, 2020

Very good and concise course. I would, however, propose to delve more into theoretical mathematics and explain them with more detail as it seemed to advance very fast.

Aug 02, 2017

This course helped me a lot in getting a better understanding of Bayesian methods. I recommend this course for all data scientists and machine learning practitioners.

Feb 19, 2019

Great introduction to Bayesian statistics. Very helpful for me, especially for understanding some of the times when priors might be useful, and how they can aid me.

Jul 01, 2017

Taking this course hase been fun. The material is presented in a clear and structured way, the Tests help to understand and deepen the knowledge. I can recommend it.

Aug 27, 2017

very good course with good concept and work. Content is very rich. Assignments are very good. It was very helpful for me. Thanks for providing such a good course.

Apr 10, 2020

Overall a great course! The honors assignments helped deepen the understanding of the concepts, and weren't just extra work.

The instruction videos are a bit dry.

Aug 17, 2017

Great course. I was more confident in frequentist than Bayesian one so, I found this course very enlightening for me and topics' structure has never been boring.

Aug 01, 2017

Necessary concepts are reviewed to the necessary depth. This is a rigorous yet light material that presents statistics on university intermediate/advanced level.

May 16, 2017

A concise and clear introduction to the Bayesian paradigm. Its conciseness make it suitable for frequentists wanting to get a quick overview of the Bayesian Way.

Dec 02, 2017

Thank you so much, Herbert Lee. I really like the way you explain everything clearly and how you organizes the contents. I recommend this course for my friends.

May 11, 2020

excellent course to understand Bayesian approach. i have good idea bout prior and posterior probability, predictive distribution , maximum likelihood estimates

Dec 29, 2019

The awesome course really liked the mathematically. If someone really want to understand the Bayesian statistics, they should definitely go through this once.

Jan 18, 2018

A great introduction to Bayesian Statistics for everyone who has some basic knowledge of calculus and is familiar with the fundamentals of probability theory.

Aug 21, 2017

Great introduction to the Bayesian framework! The exercises are relevant and I look forward to the second part (Bayesian Statistics: Techniques and Models).

Jan 20, 2017

A step by step course, designed to pay attention all the time with tons of practical examples and very clear explanations, I would definitely recommend it!!

Jun 12, 2020

Herbert is clear, gives great examples, and is easy to follow. The question prompts are helpful, and the quizzes thoughtful and challenging. Great course.

Sep 21, 2017

Very good course for fundamentals of Bayesian statistics. Made me understand Monte Hall problem, conditional probability, etc. in a totally different way.

Oct 03, 2016

The course creates great foundations for digging deeper into more complex concepts and trying to run some Bayesian statistics on simple real life problems

Dec 02, 2019

A mathematics course I really enjoyed because the instructor was actually teaching the material as best as one could without meeting the students. Great.

Mar 06, 2018

Thank you very much for sharing your knowledge with the public. Now I am no more afraid to face the book 'Bayesian Data Analysis' by A. Gelman et al.

May 07, 2020

I really enjoyed this course. Dr. Lee is a really good instructor. The materials and tests are good as well and will help you during the journey.

Jul 05, 2017

Great course. Intermediate to advanced level (at least for me). You must have good foundation in probability. If so, you will learn a lot. Thanks

Jul 20, 2020

Good course about bayesian! I finally understand the relationship between frequentist idea and Bayesian approach and Beta gamma distributions