The St Petersburg phenomenon. The paradoxes of probability theory. - page 14
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I've had a look. I couldn't even download a file from my computer.)
Actually, Jupiter is in Anaconda, but spyder is more interesting and has more features, in terms of development environment.
All the files are on the google drive, which means that your computer does not have all these caca
I have to connect to the virtual machine first and then boot up.
I don't really need it.
Tell me about it))
I don't know) It's clear that it can be constructed by approximation, by Monte Carlo method. It is necessary to generate a lot of pieces of SB and on each of them to calculate your Hearst - you get a large sample, which gives the possibility to build some approximation, about which it is not clear how good it is.
That is why statisticians (scientists) prefer to work with those statistics (sampling functions) whose distribution for the null hypothesis (in our case: prices - SB realization) is known in analytical form.
Judging by the description, there is, and lots of it, and not just in Python.
The question here isn't even... is there something else like...?, but what exactly is needed? What is missing? One chooses from this, not from something exotic somewhere.
I realise I'm not discussing the St Petersburg phenomenon, so it's off-topic.
Python, not python... This is the question... Or is it not a thesis and we just don't understand what we want?
First - the goal,
After that - objectives,
Then the toolkit.
Don't...?
I don't know) It is clear that it can be approximated by Monte Carlo method. You have to generate a lot of pieces of SB and calculate your Hearst on each of them - you get a big sample, which gives you a chance to build some approximation, which is not clear how good it is.
That is why statisticians (scientists) prefer to work with those statistics (sample functions) whose distribution for the null hypothesis (in our case: prices - SB realization) is known analytically.
Hirst is not mine, I am closer to Pastukhov with his H-volatility.
To put it all together - it doesn't happen that way at all.)
In fact, yes - I gave up on SageMath because of problems with drawing some diagrams in R. Although it was conceived precisely for this purpose.
What about the mighty bunch?
A mighty bunch. (с)
I realise I'm not discussing the St Petersburg phenomenon, so it's off-topic.
Python, not python... This is the question... Or is it not a thesis and we just don't understand what we want?
First - the goal,
After that - objectives,
Then the toolkit.
Don't...?
Yes, the goal-it's a pain, and then everything else, no, maybe it should be like this, the toolkit should be at hand, you see it, but... you can't pick it up, there's no tweezers.
Yes, the target is a splinter, and then everything else, no, I guess it has to be like this, the toolkit has to be at hand, you see it, but... you can't pick it up, there's no tweezers.
But there's a screwdriver.
Yes, the target is a splinter, and then everything else, no, I guess it has to be like this, the toolkit has to be at hand, you see it, but... you can't pick it up, there's no tweezers.
So maybe what you really need is a screwdriver, not tweezers? I generally prefer a hammer, it knocks all sorts of things out of matter very quickly. I can't get it out with tweezers.)
Physicists generally prefer to first break it into small pieces and then analyze it. We accelerate a particle to the bulldozer's mass and then the ball splashes in different directions. That's what they study.
But there's a screwdriver.
That's a pleasure.