Maximum Likelihood Estimation (MLE) with Examples

[@anikhasan-n7e]

those who are tried and looking for MLE , Just watch believe me this is the best for MLE

[@TechLovers-in]

One of the best mathematics teacher i have found on yt

[@HinalVprajapati]

Thank you soooo much ... You didn't know in which time this video help me ... God Bless you

[@DrHarishGarg]

Its my pleasure .... Happy learning.

[@limzijian98]

You are a saviour

[@darkavenger5961]

'this is in pounds' just saved him from half the human population lol

[@DrHarishGarg]

Thanks

[@ashraysinha409]

real

[@Sambhai01]

This channel save my Career

[@CalmMindOasis-u9u]

Thank you sir for the amazing lecture.

[@Kahkashan-wo5vj]

very good and satisfactory explanation. Thank you

[@arjyadeep1818]

Thanks, u got my concept cleared 🙌

[@DrHarishGarg]

Great!... keep watching and sharing

[@avyakthaachar2.718]

Thank you so much sir for this amazing explanation 🙏

[@priyansukumar3875]

Thanku so much sir ❤️
Your content is amazing to clear concept

[@fahadmehfooz6970]

Explained it so nicely. Keep up the good work!

[@DrHarishGarg]

Thanks. Keep watching and sharing with others

[@ks3356]

Best explanation...

[@sujatasaha3043]

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)

[@ZavierBanerjea]

Both (A) & (B) will have same value. {100\choose50}({\frac{3}{16}})^{50}

[@user-cd5bt3kt7q]

Wow wow wow very nice explanation sir thank you so much 🤩

[@alisalahnezhad2884]

I am very thankful for your video
it explained so easy to understand and was perfect😍😍

[@nazmulhassanlimon]

Can u share the slide ? It will be helpful for us

[@user-hm7cf3ns2u]

Grateful for your efforts 🙏🙏🙏

[@ilmpoint5967]

Thnk u so much sir ..

[@NikitaNair]

Wow!!!

[@mdragibehshan5177]

Thank you so much, sir, explained it in a very-very easy way

[@You_Tubeee]

Quality Content ❤️

[@DrHarishGarg]

thanks dear..... Keep watching and sharing with others

[@omkar9197]

truly amazing

[@imaginics]

amazing sir

[@hamshihamsu5461]

Thank you so much 💝

[@freetime_np3284]

just awesome

[@GM63746]

Sir may I get the pdf of this lecture?

[@soumnanema8362]

THANK YOU

[@DrHarishGarg]

My pleasure... Keep sharing with other students too so that they will also learn with easiest manner... Happy learning...

[@School-of-Statistics]

Excellent

[@DrHarishGarg]

Many thanks

[@d.d.2719]

You helped me a lot sir!! Thank you sir

[@DrHarishGarg]

My pleasure always....

[@prashikgadge6405]

Thankuuu

[@hsd287]

Tx a lot

[@akashgoyal6351]

sir please explain those examples also in which we use order statistics

[@DrHarishGarg]

Yes, we will discuss surely in the next couple of videos on MLE

[@akashgoyal6351]

@@DrHarishGarg thanks sir

[@ignoumathematicsguru4701]

Nice I was student of thaper

[@cmcatholic1798]

9:50 why we haven't taken log in this example?

[@mysterious3994]

much better

[@devananda8051]

Sir, can you give me example for biased MLE's

[@sumbulsheikh31dec]

Sir plz make video on mean square error

[@a2023dj]

Thanks .

[@DrHarishGarg]

My pleasure. ...keep sharing

[@duniya-wale]

Great love you Sir❤❤❤❤

[@timangshuchetia3303]

Hi Dr. Harish can you just help me out in a research work

[@harshal1348]

Is this concept from machine learning?

[@DrHarishGarg]

It is from Statistics but widely used in machine learning.

[@vissapallavi3702]

Sir i need cramer huzurbazar theorem sir

[@bhimadhikari2846]

❤

[@Chill_as_Fire]

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.

[@parind-e-hind7311]

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.

[@Chill_as_Fire]

@@parind-e-hind7311 Thanks a lot

[@newmoviefinder3049]

At 7:16 i heard a voice like the wild fox 🦊 Anyone have noticed it ????

[@mahbubulhasan4672]

Can you write the likelihood function in the conditional form? As you have discussed in 5.45min..

[@DrHarishGarg]

Many thanks....
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t.me/gargProbability

[@karanshah1112]

❤❤☺☺

[@mhj.xd7]

You are a saviour