I'm multiple years into studying graduate statistics and I've never heard this described so simply. People jump through so many hoops trying to describe what likelihood is, but you perfectly boil it down to the essence here. I've never heard it described so simply. Thank you.
Like others, after years of studying statistics, and reading your book on Bayesian statistics and enjoying every page, I still find myself watching these videos. The pace is perfect, the content, the explanation. I remain extremely grateful for this free resource.
After years of study at superb institutions... after publishing complex papers using advanced multi-variate modelling techniques... I finally understand 'likelihood'. Thanks so much! One criticism... in the first graph, it would have come to me with fewer viewings if the graph looked like a true density (area under the two heights) instead of two straight vertical lines.
The final point you make is very helpful - "in the first graph, it would have come to me with fewer viewings if the graph looked like a true density (area under the two heights) instead of two straight vertical lines."
At 4:39 the graph you plotted for theta vs p(x=1/theta) should be normal right? assuming p(x=k)=theta^k*(1-theta)^k, then for p(x=1)=theta*(1-theta) then for all values of theta with x=1 lies between [0,1] with a mean of 0.25 and symmetric curve.
I'm multiple years into studying graduate statistics and I've never heard this described so simply. People jump through so many hoops trying to describe what likelihood is, but you perfectly boil it down to the essence here. I've never heard it described so simply. Thank you.
Like others, after years of studying statistics, and reading your book on Bayesian statistics and enjoying every page, I still find myself watching these videos. The pace is perfect, the content, the explanation. I remain extremely grateful for this free resource.
I've studied statistics for like 2 years. Finally I got the difference between likelihood and probability distribution.
from, 3 days, I was trying to wrap my head around this concept. Finally an eight minute video did the magic
After years of study at superb institutions... after publishing complex papers using advanced multi-variate modelling techniques... I finally understand 'likelihood'. Thanks so much! One criticism... in the first graph, it would have come to me with fewer viewings if the graph looked like a true density (area under the two heights) instead of two straight vertical lines.
The final point you make is very helpful - "in the first graph, it would have come to me with fewer viewings if the graph looked like a true density (area under the two heights) instead of two straight vertical lines."
Great! You let me understand the concept confusing me for a long time! Thank you!
This video is misplaced in the playlist. It should be placed before the video on equivalence relation.
This is the best video on this topic so far.
Grateful that this video exists
2:09 "alternatively if k is equal to 0* (not 1)..."
Superb -- extremely helpful -- much appreciated!
Brilliant and simple. Thank you, sir.
First view! Thanks Ben for the amazingly clear videos. Awesome work! 🏆
At 4:39 the graph you plotted for theta vs p(x=1/theta) should be normal right? assuming p(x=k)=theta^k*(1-theta)^k, then for p(x=1)=theta*(1-theta) then for all values of theta with x=1 lies between [0,1] with a mean of 0.25 and symmetric curve.
sorry i got you the p(x=1) becomes just theta after substitution.
Why do the two arguments swap when Pr is swapped for L?
Very nice explanation!
Very well explained sir. Hats off
well explained, much appreciated video!
It was perfect, thank you
Thank you!
THANKS!
I believe Episode 16 should be, chronologically, in before Episode 14.
why the origin of the x axis does not start from zero ?
It doesn't matter its just a notation or label.Instead of writing 0 you could have just labeled x with T and H representing tails and heads.
L(t|x=0)+L(t|x=1)=1 though, t=theta. Easily visualized by drawing the graph for both cases. The triangles form a unit square.
You need to study Bayesian basics.
@@AtmoStk I did. Prove me wrong please.
This is what I was thinking too, he did both x =0 and x=1 for probability, but he only did x =1 for likelyhood, which does not make sense.