Useful Probability Lesson in Monte Carlo Simulations
April 6, 2015
It is no surprise that probability blogger Count Bayesie, also known as data scientist Will Kurt, likes to play with random data samples like those generated in Monte Carlo simulations. He lets us in on the fun in this useful summary, “6 Neat Tricks with Monte Carlo Simulations.” He begins:
“If there is one trick you should know about probability, it’s how to write a Monte Carlo simulation. If you can program, even just a little, you can write a Monte Carlo simulation. Most of my work is in either R or Python, these examples will all be in R since out-of-the-box R has more tools to run simulations. The basics of a Monte Carlo simulation are simply to model your problem, and then randomly simulate it until you get an answer. The best way to explain is to just run through a bunch of examples, so let’s go!”
And run through his six examples he does, starting with the ever-popular basic integration. Other tricks include approximating binomial distribution, approximating Pi, finding p-values, creating games of chance, and, of course, predicting the stock market. The examples include code snippets and graphs. Kurt encourages readers to go further:
“By now it should be clear that a few lines of R can create extremely good estimates to a whole host of problems in probability and statistics. There comes a point in problems involving probability where we are often left no other choice than to use a Monte Carlo simulation. This is just the beginning of the incredible things that can be done with some extraordinarily simple tools. It also turns out that Monte Carlo simulations are at the heart of many forms of Bayesian inference.”
See the write-up for the juicy details of the six examples. This fun and informative lesson is worth checking out.
Cynthia Murrell, April 6, 2015
Stephen E Arnold, Publisher of CyberOSINT at www.xenky.com
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