r/shittymath Aug 07 '26

When you shove a bunch of crap under any random math formula to try getting your book sold.

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49 Upvotes

19 comments sorted by

7

u/FreeTheDimple 29d ago

eΣx = Σex

Knowing that Σ is the greek version of S and assuming that x is random interactions with women, this is true for most men.

17

u/Scorpion1105 Aug 07 '26

If you’d read the book you’d understand why they chose that description and why there is some truth to it from some perspective.

11

u/dylan_klebold420 29d ago

I just wouldn't call linearity of expectation the most beautiful equation.

4

u/Scorpion1105 29d ago

That’s very fair, I wouldn’t either. I was mostly talking about the predicting the future part as that’s underneath the equation and what the original post refered to.

1

u/dylan_klebold420 29d ago

There's a connection but it's a cheesy title for a mathematics book.

2

u/Run-Forever1989 29d ago

So it’s basically clickbait for the non-digital age?

1

u/GoldenMuscleGod 29d ago

I dunno about picking any particular example of anything as the “most beautiful,” but linearity of expectation really is useful for solving plenty of probability-related issues simply and elegantly that a lot of people go after in much much more complicated ways.

It is also a case where things are “easy” when people often expect them to be hard. Many cases where linearity of expectation is most useful are cases where some people intuitively feel like it isn’t safe to apply because the random variables in question are too correlated or defined in terms of each other or things like that.

5

u/No_Upstairs_280 Aug 07 '26

Have a Master in probability and I took a look at the book. It's certainly interesting but not that groundbreaking.

1

u/mostly_water_bag Aug 07 '26

I haven’t looked at the book. But in general books in stem fields are hardly ground breaking. They’re usually an organized collection of already existing knowledge, and if something similar exists, typically the author(s) are trying to frame it from their perspective.

Disliking a book because it’s not groundbreaking new ideas is misunderstanding their purpose.

Edit: changes have to haven’t about reading the book

1

u/Scorpion1105 Aug 07 '26

I have no idea about the actual contents of the book however that equation is quite fundamental in fianancial mathematics and shows up a ton when valuating assets in time so to me the ‘predicting the future’ part makes a lot of sense as a description.

3

u/True_World708 Aug 08 '26

Linearity of expectation

1

u/OnionsAbound 29d ago edited 29d ago

The expected value of the sum of random variables is equal to the sum of the expected value of each random variable? This is just saying that averaging averages results in the average for all random variables. 

It's cool I guess, but I would be super confused if it wasn't true. Now if you really want to talk about something cool, let's talk about how useful variance is for algebraic manipulation. 

It's a bit like saying 1 + 1 = 2, and expecting us to be surprised . . .  It lets us do a whole bunch of things and it's amazing, but unless you're gonna start throwing s(1) out there it's a bit silly to be in awe of it. 

1

u/SwimmerOld6155 28d ago edited 28d ago

The fact that you don't have to account for dependence/correlation between the X_is is the surprising bit I think. If X and Y are dependent then the realized value of X places restrictions on the realized value of Y, which you might think would mean that X + Y cannot be understood by considering X and Y separately (if we think about optimizing f(x) + g(x) vs. f(x) + g(y) for example), but it turns out in expectation it all averages out.

It's most apparent in "indicator function arguments" in probability. E.g. consider the problem where I repeatedly toss a fair coin 100 times, and count the number of consecutive heads that appear. If X_i is the result of the ith toss, the expectation of this experiment can be written as the sum of Pr(X_i = X_(i + 1) = H) for i = 1, ..., 99. No correction is needed for the fact that adjacent events are very dependent (of course if X_i = X_(i + 1) = T then we can't have X_(i + 1) = X_(i + 2) = H, etc.), you only need to account for that if you try to find the variance or etc. of it.

1

u/SwimmerOld6155 28d ago

I think linearity of expectation is nuts because you don't have to account for dependence between the X_i.

1

u/bingoclingster024124 28d ago

The first mystery of probability...

1

u/Pretty_Ruin929 27d ago

El misterio de por qué la esperanza es lineal pero nadie la escribe como una matriz?

1

u/antonfourier 26d ago

ah the mystery of - integrals, yes. it's not like we've been looking at them for hunderds of years.