39 - The gamma distribution - an introduction
This video provides an introduction to the gamma distribution: describing it mathematically, discussing example situations which can be modelled using a gamma in Bayesian inference, then going on to discuss how its two parameters affect the shape of the distribution intuitively, and finally ending with a derivation of the mean of the distribution.
If you are interested in seeing more of the material, arranged into a playlist, please visit: • Bayesian statistics: a... Unfortunately, Ox Educ is no more. Don't fret however as a whole load of new youtube videos are coming soon, along with a book that accompanies the series: www.amazon.co.uk/gp/product/1... Alternatively, for more information on this video series and Bayesian inference in general, visit: ben-lambert.com/bayesian-lect... For more information on econometrics and Bayesian statistics, see: ben-lambert.com/
Пікірлер: 48
Dude, THIS is the kind of education worth paying for, not whatever my professors are doing
Thanks for the video! The textbook I have been using covers the expected value for gamma far too quickly. You really laid it out and made it easy to comprehend!
Superb. Thank you for giving me the intuitive understanding that I needed in just 14 minutes (was watching at 1.25x). The stuff in other places was so dense as to be practically counterproductive; I pored over "recommended" texts for hours before a search online led me to this beauty. Very very grateful for your generosity. Two important points: [1] At 08:16, I'm pretty sure you meant to say "Y" instead of "lambda". I don't know why nobody's pointed this out for 6 years. If I'm not wrong, perhaps you could add a simple note / annotation at that point on the video so that others don't get thrown by that minor blip in an otherwise superlative effort. [2] The freehand graphs, especially the one for alpha = 3, didn't seem very reliable. So it gave me a lot of confidence to see the MATLAB projections. Please never stop using those!
omg I was struggling with this gamma distribution for a week now. thanks!
That was a great explanation. Thank you!
Brilliant explanations - thanks a lot! Can you take over from my uni lecturers please?
gotta like this, so new updated explanation can come :P
Thank you so much, great lesson!
AWESOME, no crap, straight to what we need to know!
@nasgaroth1
3 жыл бұрын
Man, this guy explain nothing, simply theoretical crap. Where is an example, some information from practice ? Nothing simply theoretical shit
What the fuck is going on
@mkrcy
18 күн бұрын
xd
thank you very much, great video and explanation
Brilliant explanation !!!
Really helpfull, clear english, great expression
Excellent explanation, thank you
Best explanation ever. Thank you.
Fantastic and very clearly explained! Thank you :)
brilliant explanation
Im confused. When changing values of alpha, you say anything not a function of y cancels out- you take the Beta^alpha part out. When changing values of Beta, you keep the Beta^alpha bit in? This still is not a function of y.
No way I clicked on this video expecting shitty audio but you sound fantastic. Very rare very rare…
Brilliant.
very nice video. bravo
such a beautiful explanation!
love the way he says beeta
thank you!
could you please tell me the name of the software he used to draw those functions
@TheXxsayaoxx
3 жыл бұрын
MATLAB?
@internetroamer8063
3 жыл бұрын
it is MATLAB
If this is Ben Lambert, then why didn't you post this video on your page as well? Would have been easier for us since it would reduce the search time. Btw your videos are a life saver.
Really very helpful
you're awesome
good video
Thank you
@oxeduc4209
9 жыл бұрын
Hi, thanks for your kind words. Glad you found it helpful! Best, Ben
can you share the matlab code with me please
very helpful. Thank you
Nice explanation!
You saved my ass.
Hey its good explanation, thanks budd, I can now understand what the actual fuck is going on. But there's one question, shouldn't the Beta be replaced in the p.d.f. by 1/Beta? Since when the parameter Beta increases, the change in specific p.d.f. should be exactly the opposite of your graph. **update Sorry I was wrong. It's just different presentation of the parameter. Thanks!
@malintha
6 жыл бұрын
Here beta represents the rate of change. I think in the other way beta representation the time for the next event to occur.
Brilliant~ you
very helpful
Can you please start from the base, I didn't understand anything.
Either I’m dumb as fuck or I’m just not paying attention like WTF if I wanted school I’ll go back for that lol more simple please
assist me in this question let x be a bin(2,p) and y be a bin(4,p). if the pr(X>1)=5/9. find pr(Y.1)
Thank you