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Added math labels to posts that use mathjax

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Brandon Rozek 2020-12-22 10:20:51 -05:00
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content/blog/2017-03-14-monte-carlo-pi.md
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@ -21,6 +21,7 @@ mf2_syndication:
format: aside
kind:
- note
math: true
---
Using Monte Carlo methods, we can create a simulation that approximates pi. In this post, we will go over the math behind the approximation and the code.
@ -126,4 +127,4 @@ We found an approximation of pi using the Monte Carlo methods! I find that reall
1) We don’t keep track of double counting. One possible solution for this is increasing the radius and bounding box appropriately so that the probability of double counting is low.
2) Speed. The more trials you ask it to run, the longer it takes to perform all of the simulations. One possible way around this is to write a parrallel version of this code. That’s possible because of the equal variance that we spoke of earlier. Pooling the successses and trials will still result in a good approximation.
2) Speed. The more trials you ask it to run, the longer it takes to perform all of the simulations. One possible way around this is to write a parrallel version of this code. That’s possible because of the equal variance that we spoke of earlier. Pooling the successses and trials will still result in a good approximation.