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Tutorial Proposal: Stochastic Simulations in NumPy #184

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weisscharlesj opened this issue May 29, 2023 · 1 comment
Open

Tutorial Proposal: Stochastic Simulations in NumPy #184

weisscharlesj opened this issue May 29, 2023 · 1 comment
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@weisscharlesj
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I would like to propose a stochastic simulation tutorial that teaches using random variables generated from NumPy to simulate various situations and solve problems. The general outline for this tutorial is below.

  1. Primer on generating random variables in NumPy
  2. Birthday problem (https://en.wikipedia.org/wiki/Birthday_problem) – determine the probability of two students in an elementary class of 20 having the same birthday by repeatedly generating random collections of 20 birthdays and seeing how often there is a match
  3. Calculate a value for pi using random numbers
  4. Simulate pathogen infections using a simple SIR epidemiology model (https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology)
  5. Simulate radioactive decay to determine the amount of radioactive materials left after a given amount of time
@rossbar
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rossbar commented May 29, 2023

These sound like great topics to me, though I suspect this is enough material to be split into multiple notebooks!

One important thing to keep in mind is how best to highlight the numpy-specific features within the problem, for example tutorial material should use the random.Generator interface. Aside from that, a hearty 👍 for these topic proposals!

@rossbar rossbar added the question Further information is requested label May 29, 2023
weisscharlesj added a commit to weisscharlesj/numpy-tutorials that referenced this issue Jun 24, 2023
Added tutorial-stochastic-simulation.ipynb file which demonstrates using random numbers to perform stochastic simulations of various processes. This tutorial was proposed in issue numpy#184.
weisscharlesj added a commit to weisscharlesj/numpy-tutorials that referenced this issue Jun 24, 2023
Added tutorial-stochastic-simulation.ipynb file which contains a tutorial demonstrating using NumPy random number generators to stochastically simulate a variety of processes. This tutorial was proposed in issue numpy#184.
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