Sum Rule, Product Rule, Joint & Marginal Probability - CLEARLY EXPLAINED with EXAMPLES!

This tutorial explains various types of probabilities (Joint, Conditional, and Marginal) and also the rules (Sum, Product, and Bayes) to compute them. The tutorial also shows the derivations and formulations of these rules.
Most of the Bayesian statistics is based on the consistent applications of these rules. Therefore, having a good understanding as well as knowing how to apply them is of critical importance.
The example used in this tutorial is taken from chapter 1 of Dr. Bishop's book.

[@vishweshkumaraithal9779]

This is perhaps the most intuitive video on joint, conditional probabilities and on Bayes rule.

[@KapilSachdeva]

🙏

[@JasonBjörne89]

What a sublime explanation. Honestly I have seen so many videos and yours just drove the concept home. Brilliant!

[@KapilSachdeva]

🙏 Happy to know that it was helpful.

[@GoingData]

I already know this but this guy needs the thumbs up! Thank you!

[@KapilSachdeva]

🙏

[@SanjaliRoy]

my feedback is that this is amazing!!! wish my ML prof taught this :( this truly is one of the few videos that breaks the sum rule, product rule, join and marginal probability down so well

[@mikhaeldito]

Your videos on probability are the first that made everything clicked. Thank you so much. I hope you will make more videos on probability, esp. regarding the various distributions, in the future.

[@KapilSachdeva]

🙏 … yes most likely a series on foundations for probability and statistics.

[@muhammadazeem2127]

Thank you so much, First I read the 1st chapter of this book and then I listened to your video. You gave a superb explanation and cleared all doubts. Thanks for your community service :)

[@ssshukla26]

Thank you so much. I hope the lessons keeps coming. This is like blessings.

[@KapilSachdeva]

🙏 Sunny, very kind of you to say this. I will try my best to be consistent from now onwards and if not yell at me :) ...keep me accountable.

[@ssshukla26]

@@KapilSachdeva Sir, we are grateful to you for these videos. Keep them coming, consistently or inconsistently is just a matter of perspective. Have a great day ahead.

[@badstylecherry7255]

Nicely paced, beautifully animated, taking the time to fill in concrete indices somehow makes it easier for my smooth brain to grasp. Additional notes on where the name for marginal probability comes from are also greatly appreciated. Thanks for this tutorial. I’m sure I’ll be returning to your channel for more elucidation.

[@KapilSachdeva]

🙏

[@mabelgonzalezcastellanos3442]

Thanks for making it that simple!

[@KapilSachdeva]

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[@user-or7ji5hv8y]

This is a really good example.

[@KapilSachdeva]

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[@ShaharukhQureshiAP]

Great explanation, thanks for making life easier, More power to you!!

[@KapilSachdeva]

🙏

[@tourmaline-5864]

Very well explained, thank you so much. You helped me a lot

[@KapilSachdeva]

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[@duydangdroid]

this is so much better than grinding through practice questions to prepare for exams

[@KapilSachdeva]

🙂

[@lcfrod]

Clear explanation. Thank you so much,

[@KapilSachdeva]

🙏

[@UdemmyUdemmy]

beautiful way of puttting it together

[@KapilSachdeva]

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[@sergiobromberg9233]

OMG! Why is this so underrated!!!????

[@KapilSachdeva]

🙏

[@krgonline]

nice videos. pls keep up the good work

[@KapilSachdeva]

🙏