Zurück zu Bayessche Statistik

3.9

Sterne

648 Bewertungen

•

204 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 Daniel R

•Jun 14, 2017

they didn't tell that this course didn't have a homework, reading, or practice problems to do. Ended up s

von Minasian V

•Aug 16, 2016

This course was the most challenging one among all courses in specialization. I wish there were more explonation of how we get smth from smth and not like " and it appears to be equal .." and so on.

I also had to watch Ben Lambert's bayesian course on YouTube to understand the material of the second week,because Prof. David Banks was not good enough in explanation.

Assistant Prof. Mine Çetinkaya-Rundel has an amazing teaching skills.

Prof. Merise Clyde is good in explonations and I understand that she tried to present a very complicated material in a simple way, but as I have already mentioned above, I wish there were more explonations of casuality of the formulas with examples and Intuition that stands behind these formulas like in Ben Lambert's videos.

Many thanks for such an amazing experience.

von Andrea P

•Nov 12, 2016

Very interesting and formative. It starts from the basics (Bayes' theorem) but then it goes beyond the usual conjugate models such as Beta-binomial and Gamma-Poisson. Bayesian Linear Regression, Bayes Factors, Bayesian Model Averaging and a brief introduction to MCMC are provided. This really put me in the position of applying Bayesian Statistics to some real world application: the final test case is a good illustration. The only minus is that the part on Bayesian Hypothesis Testing (in particular Bartlett's and Lindley's paradoxes) is a bit rushed up, and not as clear as the rest of the course. All in all, a really good course, I'm glad I followed it.

von Do H L

•Jul 09, 2016

I beta-tested this course and it was an amazing experience. The instructors are super engaging and upbeat, despite having to delivery a very complex subject like Bayesian statistics. The quizzes are very rich, with a nice balance of practice quizzes and graded quizzes. The practice quizzes offer very helpful explanations that can help to reinforce understanding before doing the graded quiz. Last but not least, the final project description is super exciting and thorough. I highly look forward to completing this course and get a deep introduction to Bayesian statistics.

von Jonathan N

•Oct 23, 2016

Outstanding material. It may be the hardest level compared to the rest specialization course, since Bayes indeed have high technical level detail. But it was worth it. Great course and detailed from the instructors.

von Nazir A

•Sep 20, 2019

Excellent coverage. Needed to read up before watching the video in order to be able to follow the concepts. Topic was covered extensively and I was able to learn a whole new way of looking at statistics in general.

von Andre G L O

•Feb 05, 2017

For sure the most challenging course so far.

I'm amazed by how our statistical intuition fits with Bayesian approach and how we can get better results.

I'm eager to use this concepts in new models at my job!

von Roland

•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.

von Graeme H

•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.

von Lucía M F

•May 27, 2020

Es un curso muy difícil de seguir si no tienes amplios conocimientos de estadística y probabilidad. Nada que ver con los anteriores de esta especialización.

von 魏震

•Nov 17, 2016

Very nice introduction to bayesian statistics, the materials have some level of depth, and the tests and assignments are highly available for beginners.

von franco g

•Dec 16, 2016

This is my first course on bayesian statistics, I really like it, it was step by step, and helps to clarify lots of concepts of frequentist statistic.

von Shao Y ( H

•Oct 30, 2017

The course is compact that I've learnt a lot of new concepts in a week of coursework. A good sampler of topics related to Bayesian Statistics.

von 殷子涵

•Jan 06, 2017

This course is very good for bayesian statistic theory. What is more, it also teaches a lot of coding skills with R which is really useful.

von Raffy S C

•Mar 22, 2020

Great course. Quite difficult though. I wished it was split to two course or maybe an entire specialization dedicated for this.

von Mark P

•Oct 24, 2017

Slightly math heavy at times but the practical labs were awesome. I thoroughly enjoyed the final modeling assignment as well

von Michael B

•Oct 26, 2016

Great course with clear instruction and a final peer-review project with clear expectations and explanations.

von Melesio C S

•Aug 22, 2019

It was a very interesting course, i really recommend it if you want to get into bayesian statistics.

von Jinwoo L

•Jul 11, 2019

Literally best concise and brief explanation. Dr. Rundel wakes my intellectual vitality up again!

von Yan Q

•Dec 23, 2016

very helpful, however there is no recourse or recommend information for reference textbook.

von fanjieqi

•Jan 04, 2018

Pretty good, but some parts is a little difficult. I need to watch these videos more.

von Julio C L J

•Dec 19, 2017

The course was hard, because the topic is quite difficult. I missed the readings.

von Perry C

•Sep 25, 2017

I didn't understand everything but I learned more than I ever thought I would.

von Parab N S

•Oct 02, 2019

An excellent course by Professor Rundel and her team on Bayesian Statistics.

von Zhen X

•Nov 26, 2018

Provide bunches of intuition of bayesian statistics. Worthwhile to enroll!

- 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