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This doesn't make sense to me. Look at these two sentences in the problem statement: * A person that achieves their goals has a 95% chance of feeling accomplished. * What is the probability that a person is happy given they achieved their goals? The question can be rephrased as: "Given a person has achieved their goals, what is the probability they are happy?" This makes it clear that both of these statements refer to the same prior probability, which is that the person has achieved their goals. Thus the answer is 95% (assuming accomplished == happy). Btw, thanks for the tree visualization here, that really helps clarify some things about Bayes' theorem for me.

Ask it what a woman is

really great video! thanks

Don't forget to do the procedure (like and subscribe)

Loads of value!

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Kramnik's team of mathematicians would like to contest your findings.

Nice Project Sir

Wow! Excellent, thank you so much for the explanation. The tree really helped a lot understanding the problem.

Thank you for the amazing 😍 video...Waiting for python for ml video ....

Awesome morning motivation. Waiting for video on python for mlops

Super video Dear Math . extremely gained good knowledge.Do u have any course on python for ml or python mlops . It would be most demanding video on internet .

Hello sir could you please suggest me python for mlops text book or videos or if possible kindly make video on python for ml . thank you

The best ever video on ml i saw on internet. Thank you sir for this amazing video.

this watch was an absolute gem!

Thanks man really helpful

Super helpful, thanks!

thank you sir, I was really getting understanding of inferential statistics through this course, but I got confused in the exercise, What was the question, what were we asked to find?

thanks a lot pal! dont know why its so hard to find an example on LSE with numbers. cheers!

thanks bro

super helpful. tysm

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thank u very much, "making the tree" was the tip i needed to understanding this theorem and solving an exercise

God! I love this channel!

very useful, thank you for this beautiful condense overview.