Stream: new members
Topic: A picture of [0,oo]
Lars Ericson (Dec 11 2020 at 05:20):
Just for fun:
Kenny Lau (Dec 11 2020 at 06:04):
how did you do that?
Lars Ericson (Dec 11 2020 at 14:24):
In Python in a Jupyter notebook, by hand, after picking out all the definitions, like this:
from graphviz import Digraph def show(e): g = Digraph(comment='Grundbegriffe') g.attr(shape='box') for a,b in e: g.node(a, shape='box') g.node(b, shape='box') g.edge(a,b) return g show([('probability_space', 'Steinhaus'), ('P_I01', 'Steinhaus'), ('measure_space', 'probability_space'), ('measure', 'probability_measure'), ('measure', 'measure_space'), ('probability_measure', 'probability_space'), ('probability_measure', 'P_I01'), ('I01', 'P_I01'), ('set', 'I01'), ('ℝ', 'I01') ])
The notebook is here.
I am trying to teach some friends about approximation of solution to stochastic processes using an ancient book written in a dead language. Because I don't really know anything, in particular, I don't know enough what is trivial window-dressing that I should take for granted, I am stuck on an irrelevant concept on page 3, and I thought Lean might help me clear up my thinking:
Last updated: May 15 2021 at 22:14 UTC