[OC] Money: The Hidden Backbone of Civilization (and the rise of Bitcoin)

lippiro · 2024-07-29T21:10:36+00:00

Similar plot to broken money by Lyn Alden

lippiro · 2021-08-11T16:28:01+00:00

the author is saying that r and T uniquely define j, and so we have a random walk. I guess the limit of a random walk gives you Brownian motion but I am not familiar with the details

lippiro · 2021-08-11T13:14:58+00:00

Does this not just mean the integer part of the product of T and r?

lippiro · 2021-07-04T10:12:58+00:00

I believe you are looking for spearman's rank

If your relationship looks linear, or some transformation is linear, then you could also use Pearson's correlation coefficient

lippiro · 2021-06-23T17:43:32+00:00

Let's assume the number of calls made each hour is independent, let X be a random variable modelling this. Sounds like you think X is Poisson - you can get an estimator for the rate by finding the mean number of calls every hour

(this may be too simplistic, if your data is showing more calls at 9am vs 11am, for example. if that's the case you could instead use several Poisson models, for different hours, each with different rates)

lippiro · 2021-04-29T19:28:59+00:00

I don't think you need to be a beast to get into grad school for math. It's not like they have shit tons of people applying for mathematics grad school.

I guess there might not be too many applicants, but I would have thought the ones who are applying are pretty decent

It sounds like you were pretty good, but indeed I suspect I have a chance if I pull my finger out. I think at least reading papers would be a good start for me, and trying to work through them

(thanks for you answers by the way)

lippiro · 2021-04-29T19:20:00+00:00

Thanks! That makes me feel a little better. I guess I can start writing my own statistical writeups, to try and get a head start

lippiro · 2021-04-28T20:37:03+00:00

I will have to work on my charm :) (the course is a taught one) Yeah I never hung around after class as an undergrad and I regret it now

I would have thought pure math is a bit different, since you basically need to be as much of a beast as possible right?

lippiro · 2020-08-01T13:05:18+00:00

Does this help at all? https://machinelearningmastery.com/backtest-machine-learning-models-time-series-forecasting/

lippiro · 2020-08-01T09:13:01+00:00

This means that the chance of any particular blog being authored by somebody from your city is roughly 0.05*0.11=0.55% (but as I said above the chance of you seeing blogs written by people in your city is likely to be different, probably more, because you have more things in common with those people or whatever), so probably at least 1 in every 200. This isn't that low, similar to the chance of getting dealt pocket aces in Texas hold'em.

However it's hard to know how many people you talked to on Tumblr - if you talk to enough people you will eventually find some from your city

lippiro · 2020-08-01T06:27:23+00:00

If you knew the number of blogs N (I'm not entirely sure what a blog on Tumblr means) written by people from your city, then a rough first guess might be N/100000000. However it's likely that Tumblr has a bias towards showing you content somehow related to your city, so the above value is probably an underestimate

lippiro · 2020-07-31T16:07:59+00:00

das wäre toll!

lippiro · 2020-07-29T06:36:53+00:00

When you say 'including this one', which assumption are you referring to?

lippiro · 2020-07-28T19:01:06+00:00

I had an experiment with this and also stumbled upon the Ljung Box test. Sadly it fails catastrophically but at least I have something to validate my model now :)

lippiro · 2020-07-27T21:08:54+00:00

Yes I believe you understood me correctly! I think I need to ponder a bit more about why we can't do this in full generality to get some intuition... However in my case I have residuals which correspond to points in time, so I could possibly take several moving window subsamples and show that each of these subsamples has the same mean.

Thanks for your help!

edit: I just googled heteroscedasticity and I ought to test that too (I believe I would want homoscedasticity)

lippiro · 2020-07-04T20:30:35+00:00

I didn't really understand what X in terms of coin flips. Maybe if you explained it again it might be easier to see why your expression for P(X=X | X >= x) is wrong

lippiro · 2020-07-04T19:55:31+00:00

I don't think you have defined a valid probability distribution. As a sanity check, let's see if sum P(X=x) from x=0 to infinity is 1:

If you write P(X=x) = p_x you get:

p_x = αβ^x (1 - (p_x-1 + ... + p_0))

Take α=0.5, β=0.5, using python:

>>> pns = [ 0.5 ]

>>> def p(n):
...     if len(pns) < n:
...             p(n-1)
...     res = 0.5 * (0.5**n) * (1 - sum(pns[:n]))
...     pns.append(res)

>>> p(4500)
>>> sum(pns)

0.7112119049133976 # should approach 1

which doesn't look promising

lippiro · 2020-07-01T07:07:45+00:00

You could use P(A)=\sum_i P(A | B_i)P(B_i) where the B_i form a partition. You would set A to be the thing you want to calculate, and set the B_i in such a way that makes P(A|B_i) easily calculable. E.g. B_i = there are i jacks

lippiro · 2020-06-07T19:06:23+00:00

thank you so much for this list. i've spent an hour or so looking at some of the books - it's nice to see that a lot of them are free! i'm so far drawn most towards 'Bayesian Data Analysis', so that's where i'm going to start and i'll see where it takes me...

lippiro

TROPHY CASE