Moments of Distributions

MIT RES.TLL-004 Concept Vignettes
Instructor: Sanjoy Mahajan

This video will show students how to calculate the moments of a distribution and how moments can help us understand something about a distribution.

License: Creative Commons BY-NC-SA

[ledescendantdeuler]

"Throwing away information is the only way to fit the complexity of the world into our brain. The art comes in keeping the most important information." 2:16

[wentaofeng]

i really respect my professor, but man the guy in video is 100 times better in every

[caseyli]

That moment (ha!) when you've been blundering through 5 years of statistics classes and you finally understand what a moment actually is. This was a super clear video and I liked the airport example.

btw the "(something I can't understand)" in the subtitles at 9:04 is "rises to a peak in the middle".

[CuongHut]

"Throwing away information is the only way to fit the complexity of the world into our brains " - Sanjoy Mahajan

[scottmacnevin]

I found Sanjoy very easy to follow, simple but effective examples provided, thank you!

[JeffBagels]

This guy is awesome. Explains things clearly.

[kevinhevans]

Thought that was a good explanation! I'm just now learning about this in a statistical physics course and it's nice to have a non-physics explanation of it. :)

[wisescouncil]

I really love how he casually makes reference to other discipline

[tahsinl]

Thanks for the lecture. It was very helpful for studying for my engineering probability exam.

[DavidAndrewsPEC]

I found this okay - interesting topic, and a lecturer who knew the topic well enough to teach it in a logical and informative way.

[TheAhmedMAhmed]

super cool, super awesome!
thanks for the lecture MIT!!!

[shreehimanshu]

I want to be teached by this kind of teacher, Respect.

[Qongrat]

The video is great though one little caveat: the mean (μ or x̄ ) is not the same as an expected value (E[X]). Statements like this make things just more difficult and confusing.

The (arithmetic) mean is the sum of of all values of set X = { x1, x2, ... xn) divided by the total number of values in the set. It refers to any existing in reality (known or unknown to us) set or lists of values, like samples, populations and so on.

Expected value (E[X]) refers to an event in the future and is the most likely probability of what we can expect to get as a value in the said future event.

Yes, if you are considering drawing a sample with a large enough n, what you can expect more or less is that E[X] = μ (the mean of the population) given that you are looking at discreet or continuous distributions, but E[X] = p (proportion of the population) if you are looking at binary Bernoulli distribution.

[dhritishmanhazarika]

That's an AI programmed professor. The eyecontact and the way he is speaking. Cool.

[orjihvy]

This is really well explained. Really insightful.

[TheRealLukeOlsen]

Wow. To calculate the moment of inertia about a parallels axis is actually the 2nd moment subtract the 1st moment squared??? The moment all moments come together.

[zes]

not often, no such thing as fullx or comx or dependx, can say any nmw is ok

[dianamorton]

Such an interesting explaination! Love it

[vaishnavipal]

I hope someday MIT OCW uploads a video on Moments and Centre of mass in Calculus not the one in Rotational Dynamics

[vagabond]

This was really cool and really helpful. Thanks.