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Avis et commentaires pour d'étudiants pour Bayesian Statistics: From Concept to Data Analysis par Université de Californie à Santa Cruz

4.6
étoiles
2,915 évaluations
758 avis

À propos du cours

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....

Meilleurs avis

GS

31 août 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

16 oct. 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

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651 - 675 sur 752 Avis pour Bayesian Statistics: From Concept to Data Analysis

par alaaeldin A

28 févr. 2017

I could say , it is the best bayesian stat I ever experience till now.

par Abhimanyu R

22 févr. 2019

This is a good course if you know probability and want to practices

par Kamil S

29 avr. 2018

Excellent course, but the lack of the written notes is a big minus

par David M

19 sept. 2017

Satisfied with the course in general. Good investment of my time!!

par Sergio E M P

13 mai 2022

es una buena heramienta de aprendisaje pero tiene algunos errores

par FG

6 avr. 2020

Good introduction, but there is no variety in the test questions

par Eddie C

8 janv. 2020

Quite harsh but give me some insight on prediction and estimation

par Eddie G

21 avr. 2019

It would have been better to have more data analysis applications

par Chunhui G

7 mars 2019

These are a lot of stuffs that the professor didn't say clearly.

par Toan H

15 mars 2021

The section on regression can use a bit more Bayesian treatment

par Aravind M

17 avr. 2019

Good introductory course. Could provide more hands-on examples

par Juan C C E

23 févr. 2020

Explain with more details the concepts, the mathematics is ok

par Dziem N

22 juin 2020

I wish there are lecture notes to accompany the videos.

par Wate S

23 déc. 2017

For me Chinese, it 's not easy to understand the quiz.

par Vittorino M C

17 juil. 2020

Well explained, I fit all the gaps about probability.

par Gil S

3 mars 2019

Clear and consise introduction to Bayesian statistics

par Yuan R

5 nov. 2016

Good and simple introduction for Bayesian statistics.

par sunsik k

23 août 2017

well instructed basic course of Bayesian statistics.

par Alexei M

13 mai 2018

More examples are required as well as more practice

par Venkataraghavan P K

10 févr. 2019

Loved the theory & analytical part of the course.

par Bishal L

7 mars 2017

It is a nice introductory course on Baysian s

par JhZhang

14 mars 2020

深入浅出,结合理论推导、实际应用与直观理解,挺好的一门课,让我对贝叶斯推断有了极大的兴趣

par Carson M

27 oct. 2017

Pretty good overview of Bayesian statistics.

par xuening

25 janv. 2017

from week 3, the learning curve become steep

par Wenbin M

9 févr. 2020

The normal distribution part lacks detail.