7:15 Can somebody please explain in layman terms how the probabilities of 1/2, 1/3 & 2/3 were reached for the heads flip (given 61 heads for 100 flips) even ratio of heads & tails is 60:40 which would be 3/2 NOT 2/3.
Suppose you have a weighted(biased) coin with a probability of heads 2/3 and tails 1/3. Now, If you flip it 3 times, it is still possible to get tails 2 out of 3 times, Right? Similarly if you flip a fair coin 3 times with probability of heads 1/2, you can still get 3 tails. Basically, There can be a difference between observed favourable events and calculated probability of favourable events, Which is whats happening here. Probabilities of 1/2, 1/3 and 2/3 are arbitrary and they are different from the observed counts of heads.
A coin is tossed 100 times independently. 50 heads are obtained. Let p denote P(H) It is given that p \in \{1/4, 3/4\} Then MLE of pis: (A) 1/4 (B) 1/2 (C) Does not exist (D) Both (A) and (B)
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'this is in pounds' just saved him from half the human population lol
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Thank you so much, sir, explained it in a very-very easy way
9:50 why we haven't taken log in this example?
amazing sir
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sir please explain those examples also in which we use order statistics
Yes, we will discuss surely in the next couple of videos on MLE
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Sir may I get the pdf of this lecture?
7:15 Can somebody please explain in layman terms how the probabilities of 1/2, 1/3 & 2/3 were reached for the heads flip (given 61 heads for 100 flips) even ratio of heads & tails is 60:40 which would be 3/2 NOT 2/3.
Suppose you have a weighted(biased) coin with a probability of heads 2/3 and tails 1/3. Now, If you flip it 3 times, it is still possible to get tails 2 out of 3 times, Right? Similarly if you flip a fair coin 3 times with probability of heads 1/2, you can still get 3 tails. Basically, There can be a difference between observed favourable events and calculated probability of favourable events, Which is whats happening here. Probabilities of 1/2, 1/3 and 2/3 are arbitrary and they are different from the observed counts of heads.
@@parind-e-hind7311 Thanks a lot
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Sir plz make video on mean square error
Sir i need cramer huzurbazar theorem sir
Nice I was student of thaper
Sir, can you give me example for biased MLE's
Hi Dr. Harish can you just help me out in a research work
Is this concept from machine learning?
It is from Statistics but widely used in machine learning.
At 7:16 i heard a voice like the wild fox 🦊 Anyone have noticed it ????
nice observation bhi
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Can you write the likelihood function in the conditional form? As you have discussed in 5.45min..
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A coin is tossed 100 times independently. 50 heads are obtained. Let p denote P(H) It is given that p \in \{1/4, 3/4\} Then MLE of pis:
(A) 1/4
(B) 1/2
(C) Does not exist
(D) Both (A) and (B)
Both (A) & (B) will have same value. {100\choose50}({\frac{3}{16}})^{50}
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