Zurück zu Bayessche Statistik

3.9

Sterne

625 Bewertungen

•

191 Bewertungen

This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm. The course will apply Bayesian methods to several practical problems, to show end-to-end Bayesian analyses that move from framing the question to building models to eliciting prior probabilities to implementing in R (free statistical software) the final posterior distribution. Additionally, the course will introduce credible regions, Bayesian comparisons of means and proportions, Bayesian regression and inference using multiple models, and discussion of Bayesian prediction.
We assume learners in this course have background knowledge equivalent to what is covered in the earlier three courses in this specialization: "Introduction to Probability and Data," "Inferential Statistics," and "Linear Regression and Modeling."...

Sep 21, 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.

Apr 10, 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.

Filtern nach:

von Pouya Z

•Sep 26, 2019

The course was great and really informative. Particularly, it was interesting to get to work with BAS and statr packages that were developed, essentially, by the instructors. I, however, think that from decision loss functions onward, the course suddenly became way more complex. The normal conjugate families were not discussed on the previous lab, and I believe deserve to be emphasized with an example before heading to regression and reference priors. However, the notes were quite helpful. All and all, it was a great course.

von Ravichandran V

•Aug 06, 2019

Its really hard for me to follow this specific course, its as if I am reading a summary of a novel rather than a novel, ideally this course should be broken into two courses and made into two five week courses. I may need to take additional courses or read some books to get a clear understanding.

In the previous three courses the open stats book helped a lot, however, the online content for this course is difficult to follow as well.

von Uira d M

•Jul 03, 2019

The course is well structured but the span of topics is large and the complexity great. Maybe an extended version with more explanations and demonstrations of the equations would be better for understanding the whole concept of bayesian statistics, specially inference.

von De'Varus M

•Feb 15, 2019

Though this section in the specialization is a little more difficult than the other sections. The supplemental material provided is helpful in navigating through the course. I will continue to read through this material to further my understanding of the material.

von Francisco M

•Nov 09, 2019

The course is very interesting, but the jump from the previous course is too large. From calculating probabilities by hand and understanding the odds involved, to integrate distributions is too abrupt and not explained in detail.

von Janio A M

•Aug 09, 2018

Its a tough course however, I will just suggest to focus more on the practical side of things after doing all the theory. I really enjoyed course 4 where the professor used "R" to compare different models using the crime dataset.

von zhaokai

•Aug 16, 2016

I hope we can have access to the slides, and this can save attendees a lot of time, because I think after we finished watching the video we can skip relevant slides when we come across problems in doing exercise.

von Mark F C

•Jun 21, 2018

It was a good course, though I would include more coursework and exercises in R to assist with comprehending a difficult subject. Overall, good course for something that's difficult to teach.

von Kian B

•Jul 29, 2016

The section about Beta-Binomial Conjugate is taught very fast and unless the student is quite familiar with Beta and Gamma distributions, it makes it very difficult to follow the course.

von Jae S P

•Jul 18, 2017

This is one of many good courses that one can get a glimpse of Bayesian statistics though it lacks of thorough explanation of mathematical background and reading materials of any kind.

von José L E N

•Jan 03, 2017

Theis course is substantially more difficult than the three first ones, and the material is scarce. However, I must admit that this is one of the courses I have ever learnt the most

von Stanley R C

•Jan 30, 2018

The instructors have great expertise, but this course is pretty difficult for a Bayesian newbie. Additional study guides would be helpful (especially week 4).

von Lalu P L

•Jun 02, 2019

The course could have been more comprehensive and less verbose. It had so much content in a tiny course. Content should be less and more comprehensive.

von Malolan S

•Sep 10, 2019

A bit more depth in explaining conjugacy in priors and posteriors will be very helpful. A possible way would be to have more example illustrations.

von Ángela D C

•Jun 12, 2018

Week 3 was too much information too soon, but week 4 was great again like the other courses in this specialisation. Learned so much, thanks!

von KALYESUBULA M

•Jun 03, 2017

Learnt a lot. Though the subject material was hard to grasp first hand, it is good that instructor was readily available to help us through.

von Adam A

•Aug 25, 2017

An interesting and challenging course, would be better with more real examples and explanation as some of the material felt rushed

von Marwa A E K M A Z

•Jan 07, 2020

It's a good one, but not as previous courses. Week 3 isn't well explained as other weeks. Hope it can be further improved

von Hanyu Z

•Dec 08, 2016

The material is good. However, there is no support from the instructors to answer our questions in the discussion forum.

von Niels R

•Jul 06, 2019

This course through the material too fast. The content should have been spread out over two courses in my opinion.

von Emmanouil K

•Aug 16, 2017

This is a very interesting topic. Lectures in weeks 3 and 4 could use some work.

von Vicken A

•Dec 29, 2016

Bayesian stats is a broad topic. Learners would benefit from more material.

von Jaime R

•Nov 08, 2018

Theorethical backdrop is a bit excessive on an R focused course

von Liew H P

•Jan 17, 2019

This course is challenging and well-presented!

von José M C

•Mar 22, 2017

Good content but sometimes it gets confusing.

- KI für alle
- Vorstellung von TensorFlow
- Neuronale Netzwerke und Deep Learning
- Algorithmen, Teil 1
- Algorithmen, Teil 2
- Maschinelles Lernen
- Maschinelles Lernen mit Python
- Maschinelles Lernen mittels Sas Viya
- R-Programmierung
- Einführung in die Programmierung mit Matlab
- Datenanalyse mit Python
- AWS-Grundlagen: Mit der Cloud vertraut werden
- Grundlagen der Google Cloud-Plattform
- Engineering für Site-Funktionssicherheit
- Englisch im Berufsleben
- Die Wissenschaft des Wohlbefindens
- Lernen lernen
- Finanzmärkte
- Hypothesenüberprüfung im öffentlichen Gesundheitswesen
- Grundlagen für Führungsstärke im Alltag

- Deep Learning
- Python für alle
- Data Science
- Angewandte Datenwissenschaft mit Python
- Geschäftsgründungen
- Architektur mit der Google Cloud-Plattform
- Datenengineering in der Google Cloud-Plattform
- Von Excel bis MySQL
- Erweiterte maschinelles Lernen
- Mathematik für maschinelles Lernen
- Selbstfahrende Autos
- Blockchain-Revolution für das Unternehmen
- Unternehmensanalytik
- Excel-Kenntnisse für Beruf
- Digitales Marketing
- Statistische Analyse mit R im öffentlichen Gesundheitswesen
- Grundlagen der Immunologie
- Anatomie
- Innovationsmanagement und Design Thinking
- Grundlagen positiver Psychologie