Schooling and the like

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Update - realise some bi-modal continuous distribution may be better (but potentially difficult to perform the update)

Attempt: we model the parameter pi in a Bayesian way: we put a distribution on pi (0.7 w.p 1/2, 0.2 w.p 1/2) then we weight the 1/2 with the likelihood of the observations, given that parameter (i.e. what is the likleihood when pi = 0.7, multiply that by 1/2 then divide by the normalizing constant to get our new probability for pi = 0.7 (do the same for pi = 0.2, the normalizing constant is the sum of the 'scores' for 0.7 and 0.2 i.e. 1/2 * likelihood so we can't 'divide by the normalising constant until we have the score for both 0.2 and 0.7)

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Grateful for comments here as I am not very certain on the situations that the MLE approach is better vs situations where Bayesian approach is better

Suggested answer:

c(i) Is frequentist approach where we have one parameter estimate (the MLE) c(ii) bayesian approach - distribution over parameters and we update our prior belief based on observations If we have no prior belief - c(i) may be a better estimate (i.e. in (my version of) c(ii) we are constraining the parameters to be 0.7 or 0.2 and updating our relative convictions about these - which is a strong prior asssumption (we can never have 0.5 for instance) If we do have prior belief and also want to incorporate uncertainty estimations in our parameters, I think c(ii) is better If the MLE is 0.7 then we will have c(i) giving 0.7 and c(ii) giving 0.7 with a very high probability and 0/2 with a very low probability to the methods will perform similarly

Flag: suggested answer (don't read if don't want to see a )(possibly incorrect) attempt:

attempt: MLE = k/3

Flag: Don't read if don't want to see a (possibly incorrect) attempt of an answer: (Grateful for any comments/disagreements, further points to add)

Attempted answer: Assumption is that, given a class, features are independent. We could relax this by using 2-d gaussians for our class distributions that have non-zero covariance (off-diagonal) terms so that we have dependencies between features (currently we have these set to zero for independence)

1- Do you like to play games? why or why not? I love playing all kinds of games, whether they are board games or video games. Sometimes I take the games a bit seriously because I'm a bit competitive. But they usually represent a sense of calm, focus, and fun for me at the same time.

2- What kind of games do you like to play? Now, I am a fan of starcraft 2 or any RTS game. But now I have become a fan of dota 2 and I think I'm going to give it a try. also when I get together with my friends, we play card games like Uno.

3- always i have a mate with play duo and is the same mate with i play the cards games always i have a mate to play duo and is the same mate witch i play the cards games, his name is bruno. But once a week we are playing with a most than 5 friends more.

History is focus on power

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