Two Unusual Papers on Monte Carlo Simulation


Matt Dickenson

For Bayesian inference, Markov Chain Monte Carlo (MCMC) methods were a huge breakthrough. These methods provide a principled way for simulating from a posterior probability distribution, and are useful for integrating distributions that are computationally intractable. Usually MCMC methods are performed with computers, but I recently read two papers that apply Monte Carlo simulation in interesting ways.

The first is Markov Chain Monte Carlo with People. MCMC with people is somewhat similar to playing the game of telephone–there is input “data” (think of the starting word in the telephone game) that is transmitted across stages where it can be modified and then output at the end. In the paper the authors construct a task so that human learners approximately follow an MCMC acceptance rule. I have summarized the paper in slightly more detail here.

The second paper is even less conventional: the authors approximate the value of π using…

View original post 55 more words

Advertisements

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s