Probability Primer for Probabilistic Robotics (Cyrill Stachniss)

Probability Primer for Probabilistic Robotics
Cyrill Stachniss, Summer 2020

[@milan_shah]

00:00 - Background & intro
01:37 - Recurring Terms in this Course
04:05 - State Estimation Example: Localization and Mapping
04:53 - Probabilistic Approaches
07:08 - (beginning of) Probability Primer
07:23 - Why Probabilities?
08:13 - Axioms of Probability Theory
14:17 - Discrete Random Variables
16:11 - Continuous Random Variables
18:33 - Joint and Conditional Probability
21:41 - Law of Total Probability
23:03 - Marginalization
24:04 - Example 1 (on Conditional & Marginalized Probability)
26:52 - Example 2 (on Conditional & Marginalized Probability)
32:10 - Bayes' Rule
34:34 - Bayes' Rule with Background Knowledge
35:04 - Conditional Independence
37:58 - Normal Distribution
39:10 - Multivariate Normal Distribution
40:00 - Gaussian Mixture
41:26 - Summary

[@muhammadalam4470]

[@motbus3]

Thank you professor. Very thoughtful of you to prepare a material complete for those interested in studying this field.

[@natecibik5505]

Best review of the essentials of probability theory I've seen. Thank you so much!

[@Jin-sd8qs]

Thank you very much. I really appreciate your lessons, and the effort you put in for sharing the knowledge.

[@Eng.AwsNafea]

I'm really happy to find this great channel

[@Shah_Khan]

Thanks a lot Professor for your knowledge sharing.

[@starlite5097]

Amazing channel, thanks for all the content. I am following a master's degree in Computer Vision and photogrammetry seems to be very interesting.

[@suihe]

Thank you very much for sharing.

[@CallSaul489]

Super super helpful!!

[@Eng.AwsNafea]

It was very helpful. thank you very much prof

[@umutdumandag]

Thank you.

[@jeremysrobotics191]

thank you so much

[@sohaibarif2835]

can you suggest sources to revise Multivariate Normal Distribution and Gaussian Mixture?