A clear explanation of Bayes Theorem

[@sds-superdatascience]

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[@proteuswave]

I’m really excited about this channel. You’ll have 100K subs in no time.

[@mahawewar.dimanthi810]

Thank you for the explanations

[@sohailarshad426]

defining conditional defective parts , you defined p(machin1 | defect)=50 % in simple terms , Probability of defective part given its machine one , am I correct in putting it in this way...? because what I thought here was p(Defect | Machine 1) = 50% ie Probability of a defect given its from Machine 1 ...should have been the case...!! correct me if wrong

[@sohailarshad426]

ie I am confused in understanding when is it p( defective | Machine 1) and when is it p(Machine1 | defective )

[@Cybyke]

My question is can we calculate all the Bayes theory problems using tree diagram. That seems to be more intuitive to me. I didn't understand this counting.

[@sds-superdatascience]

Hi @Cyber lyke, yes you can use the tree diagram. For example, you can use the diagram to visually represent and calculate conditional probabilities in Bayes' theorem problems. But, it is not the only method for solving such problems and other methods such as tables or equations can also be used. If you have any other questions please let us know!

[@Cybyke]

@@sds-superdatascience Thank for the clarification... Can you probably show us the Tree diagram in a very simplified way.?

[@sds-superdatascience]

@@Cybyke thanks for the idea! We'll consider it for a future video :)

[@kunjd26]

P(Defect|Mach1) = 0.83%