# Lecture: Relationship between MAP, MLE and Regularization

Dear Mentor,

I am confued when looking at this lecture.
Relationship between MAP, MLE and Regularization | Coursera

Could you please guide me on these 3 questions?

Question 1
The lecture title is “Relationship between MAP, MLE and Regularization” but the lecture only shows how regularization and maximum likelihood actually match. MAP is missing.

Question 2
At time 2:12 / 5:50, What is maximum likelihood with bayes? I thought that bayes is only related to MAP? Why maximum likelihood with bayes is equal to P(Data | Model) * P(Model) ?

Question 3
May i know which theory is this statement taken from?

At time 3:10 / 5:50 of the lecture,
Model 1 has an equation ax +b,
the coefficient a is selected from the standard normal distribution,

Then take the value, the likelihood of this Model 1 is

Thank you

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Hello @JJaassoonn,

I am going to give you some idea, but for the answers of your posted (and any follow-up) questions, it may require you to go thru the video a couple more times and/or reach out to the internet for other reference.

So below is the full picture for the connections between bayes and all other terms.

Essentially, the regularization effect comes from the prior.

Cheers,
Raymond

PS: I googled the title of this thread and some relevant results showed up!

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Dear Mr Raymond,

I still need to do more study on the Question 1 and Question 2.

May i have your guidance on the Question 3 on which theory is this statement taken from?

Thank you.

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