1,493 karma · joined December 15, 2010
"June 30, 2015 5:05 am Greece’s IMF payment: When is a default not a default?"
http://www.ft.com/cms/s/0/3344581e-1eda-11e5-aa5a-398b2169cf...
Absolutely! Default, and we'll just charge more next time. Whatever. Move on.
The motivation for this extremely anti-default push has not been clearly enough laid out, methinks. Bonds, government or not, are an investment. You think I am 100% the stocks I bought in a Ukrainian egg farm will not go bust? Hell no. All who invested in Greek government bonds should have done their homework.
I remember getting further than ever before in Midnight Resistance (which sounded less rapey to me at the time), and then waiting for the next level, loading, loading, loading...
Your work and your input will not be respected without a consideration.
(I just ego-surfed my hn name. The twitter guy is somebody else.)
2. Broadly, what area of mathematics was used - how did they go about solving it.
3. Do we have a concrete proof? For a general case?
4. Where are we now and what is next?
To expand, R has every cool statistical model (outside finance) to hand. It's deadly buzz. It's finicky as hell to program in though. Classes are a bit crap. Brackets are all over the shop. Arrows? Really? Meh. Statistical models take ages to program and test, though, and the academic paper that was published last week has had the model as an R package for 6 months.
Python is a beautiful programming language, especially for anything involving transferring data from here to there and possibly back again. I particularly like it when working with a diverse team of context experts, who would think nothing of laying a faecal nugget in your code-base, to save the time it takes to drink a cup of coffee. It's a lot harder to write crap code in Python than in many other languages.
So, in our local technical landscape, R is used for prototyping, and is used in a slave fashion for running complex stats models. Python is used as the "Controller" of the program, and also for database type operations, web-scraping, calling QuantLib, ...
Laplace has recently shown that the integral of minus t squared, where t goes from minus to plus infinity has a definite answer.
Gauss starts a paper, basically says, let's just run with the arithmetic average, the usual mean, as the go-to best estimate, from a big series of measurements of a single thing, each of which has errors, because, yeah, why not.
Moments later the guy shows that squaring the errors makes it equivalent to that thing Laplace did, and we now have a general rule. His argument follows by supposing each repeated error due to a specific cause has an expected error of zero, with no particular rule of distribution, but that as the number of specific causes goes to infinity, the Law of large numbers applies (go Euler, fair play). Hence, wiggley wiggley, Laplace's thing.
Beautiful piece of work, and resting on Laplace and Euler too, two other heroic oddballs.
The best thing is the underlying explanation: Take a nearly-infinite number of different measurements of a thing, each measurement with its own pattern of rubbishness, and the average of those measurement will approach having this distribution. That bit is what makes it so broadly applicable.
I think it provides some value, but I am not a huge fan. In my experience, the devil is in the details and the failures come during the implementation, and the management consultancies in the Marvin Bower tradition explicitly side-step implementation.