C3_W3_Quiz_Q10 How to describe the Prior and the Posterior?

Course Q&A Math for Machine Learning Probability & Statistics for Machine Learning &...
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Week 3
Week 3 - Summative Quiz

Hi everyone,

I have a question regarding Bayesian 1’s MAP in the last question of Week 3’s quiz, regarding the description of the prior and posterior. For the prior belief, should the description be “the likelihood of the coin being fair” or should it be “the probability of the coin landing on heads”? I also have trouble fitting in the the statement “Bayesian 1 strongly believes that most coins are fair” into the equation. Is this a hint that we should coming up with priors like in the lecture “Bayesian Statistics - Updating Priors” (e.g. Probability(coin is fair) = 0.75 and Probability(coin is biased) = 0.25?

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can I know where in the course it mention the probability of coin being head or tail in relation to prior and posterior?

I have never heard those terms related to probability and statistics!

this means the statements confirms that when a coin is flipped, it will either be head or tail(fair), and chances of not flipping to neither of them is very minimal(biased).

I didn’t see statement anywhere if this is your hypothetical statement for coin being fair then your probability bias for coin being biased is still high, considering it 0.001 can be probably taken into consideration.