This is NOT how the wlln and slln are differentiated, at least not how it is done in 99.99% of stochastics books.
@WagesOfDestruction
29 күн бұрын
This is a more straightforward method commonly used to solve this problem. Compare the max number found to the sum of all numbers, take the average, and double it. Then use the max of these two numbers. This method has some intuitive appeal. If the numbers are evenly distributed, the average would represent the midpoint, so doubling it could give a reasonable total estimate. I tested this out by solving a problem by estimating the highest number of houses on a street when we only knew a few of the house numbers on each street. I tested the tank method with the straightforward process, and the results were about the same.
@kestrel09
Ай бұрын
Air superiority won the war really.
@zamiyaFlow
Ай бұрын
Great video. Also I hope you sign up for a diction and modulation course to help out with that pronunciation
@VictorYarema
Ай бұрын
Wow. Your explanation is incredibly concise. Thanks!
@anmol2975
2 ай бұрын
awesome
@Ms-money
2 ай бұрын
very nicely explained!
@daddychan7
3 ай бұрын
Great job at explaining the nuances of P = 0 and P = 1. As someone studying some graduate level measure theory, I much prefer your definition lol.
@JUNGELMAN2012
3 ай бұрын
@5:39 the average gap = sum of all gaps/n. But no matter at what nr you start if x1=24 and x2=29, then the gap between them is 29-24 = 5 not 4. you claim the second gap has size (x2-x1-1).
@wisepotato69
4 ай бұрын
Such a beautiful video and the explanation was even more beautiful! Thanks!!
@sunilkumarsamji8871
5 ай бұрын
The content you made is really great but your speaking too fast and it sounds as if you are swallowing your words.....its a bit teasing as we have to pause and try to understand...may be you make the pronunciation more clearer. just my opinion, I dont know if others too felt it.
@muhammadaliimranbjalaludin653
5 ай бұрын
Great vid but can go slower for others
@zaidadarbeh298
5 ай бұрын
The video is great, it’s just one problem, the voice over. Sometimes it’s to fast, sometimes it’s unclear.
@simoncha8733
6 ай бұрын
Great video, thanks
@ds7847
6 ай бұрын
wow ! really helpful video!thanks :)
@lordcasper3357
7 ай бұрын
great video
@user-vz3pb2po4f
8 ай бұрын
very good
@joaopedrorocha5693
8 ай бұрын
Very useful! Thanks
@abekolko7143
8 ай бұрын
I have a question! I generally think of e as being fundamentally related to compounding growth, similarly to how pi is thought of as being fundamentally related to circles. So with that in mind, is there some important relationship between derangements and compounding growth? If there is, it isn't apparent to me.
@ThatisnotHair
8 ай бұрын
This is what sciéΠcë is
@jnv1971
8 ай бұрын
I applaud you as a creative, but if you want a wider audience, you should make a conscious effort to speak slower and enunciate. I'm a pretty decent non-native english speaker and I had to focus way too much on deciphering what you were saying. I gave up before the end of the video.
@Braniac57
9 ай бұрын
I understand we have to round to the nearest integer because we can’t have a non integer amount of derangements. But i guess I’m a little confused on how we know for sure that rounding will result in the right answer
@phscience797
9 ай бұрын
Don‘t worry, that was not explained in the video. The reason is simply that the approximation 1 - 1/1! + 1/2! - 1/3! - … + (-1)^n/n! = 1/e is somewhat accurate. For when you plot the alternating behaviour of this sequence, you can see that if n is even, then as the next term added is negative, you are currently above the limit, and if n is odd, you are below the limit. But the next term has only size 1/(n+1)!, which is quite small. So we know that D_n = n! (1/e + r_n), where abs(r_n) <= 1/(n+1)!, so D_n = n!/e + n! r_n, and abs(n! r_n) <= 1/(n+1). So if n \geq 2, the approximation D_n = n! / e will be accurate to within less than 1/2. As you observed, D_n is an integer, which means precisely that we can conclude D_n = round(n! / e). For n = 0, n = 1, n = 2, we can check by hand that this formula works as well.
@Md-wu3yl
9 ай бұрын
How is this related to computer science?
@astropgn
9 ай бұрын
But why the limit goes to infinity and not to the total number of the population? Are we assuming the population is infinite? I would think that if the population has a finite size, the mean of the sample would get the true mean of the population as the sample size approaches the population size
@Sheeeeshack
10 ай бұрын
Why mumble
@timbermonson
11 ай бұрын
Discovered this right after taking a combinatorics-heavy discrete math class-- great vid!
@AntonioLasoGonzalez
11 ай бұрын
Very good explanation!
@zyansheep
11 ай бұрын
(If u are a 3b1b reviewer, watch the video before reading my comment lol) I kinda got lost halfway through with all the math jargon 😅. First we were talking about hats and parties and then suddenly set intersections and permutations? The rest of the video seems to be mostly symbol manipulation and I think I'd understand it better if the actual math could be presented more closely to the context of the story. (I.e. talking about permutations of physical hats in a box, rather than items in a set)
@muhammadaliimranbjalaludin653
5 ай бұрын
Basically we consider the outcomes as ordered quadruples. For example, if there are 4 people, (1,2,3,4) can denote the outcome that everyone receives their hat. (2,1,3,4) denotes the outcome where the first person takes the 2nd guy's hat and the 2nd guy takes the first guy's head and the rest got their own head. So, you can consider the total number of ordering in this ordered quadruple to be 4!. Also, derangement here considers all cases where the i-th entry is NOT EQUAL to i, for all i.
@charlezbeatz6177
11 ай бұрын
Try to speak clearly bro....this is something that can help anyone around the world ..i had to slow the video and even then u weren't clear enough
@sanyudsouza4153
Жыл бұрын
perfect calming bg music choice tbh
@360_gangsterelite2
Жыл бұрын
Wow this is a very high quality video! Very informative!
@georgetzathas9002
Жыл бұрын
It's a shame that this hasn't gone viral like other math teaching videos. It's up there with the best in terms of quality, communication and clarity. It makes harder concepts easy to digest as every good teaching video should. Good job!
@dikshasvagarwal
Жыл бұрын
I loved your videos, especially the underfitting and overfitting one. For this one I would just say it got a bit typical towards the end to understand the concept of why exactly alpha > p- value leads to reject null hypothesis. But still great work though!!
@pettepiero
Жыл бұрын
If I could give an advice, I think you should speak a little slower for the non-fluent English speakers. Other than that, good video!
@missy7871
Жыл бұрын
well made video. thank you for helping me with stats 🙏
@ramalaccX
Жыл бұрын
Nice video and well explained. However it is so difficult to understand you. So fast and not very clear :(
@emmepombar3328
Жыл бұрын
Only good for people that already know the content. For everybody else it is too fast not very well explained. Also you speak so fast, that I had the repeat some parts of the video up to four times to understand what you were saying.
@oosmanbeekawoo
Жыл бұрын
Beautiful video! (even though I have not understood! xD)
@Tabu11211
Жыл бұрын
Subscribed
@Waterburgers
Жыл бұрын
Damn thanks, you really helped!
@srijanpaul
Жыл бұрын
Subbed. Awesome content!
@earnestmorrison8004
Жыл бұрын
ᵖʳᵒᵐᵒˢᵐ 🙋
@MrConverse
Жыл бұрын
0:01, please try to speak more clearly. I listened twice and have no idea what you said.
@JiveChip
Жыл бұрын
Need the Nstatum soda pls
@gymarcelo2822
Жыл бұрын
what softwares do you use for these videos? also good job!
@nstatum8290
Жыл бұрын
Thanks! And I use a mix of After Effects and Manim which is a Python Package
@luizhenriqueamaralcosta629
Жыл бұрын
Beautiful
@piguyalamode164
Жыл бұрын
So the weak law says that you can take enough samples so that you have an arbitrary probability <1 of seeing that sample mean be arbitrarily close to the true mean. The strong law says that, almost surely, you can make the sample mean arbitrarily close to the true mean by taking a sufficient number of samples.
@praveenb9048
Жыл бұрын
Perhaps it should be called the law of a large number of numbers.
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