Hamiltonian Monte Carlo, Parallel Tempering, MPI: lectures 14-19 of Stat 221

Stat 221 is over 75% done! We have learned how to learn about the parameters of complex models with point estimation in the earlier lectures. Lectures 14-19 are mostly about the more computationally intensive full MCMC sampling methods such as Hamiltonian Monte Carlo and Parallel Tempering. We also discussed how Decision Theory treats making decisions based on system parameters and uncertainty about them, and parallelizing computing algorithms with the Message Passing Interface:

I am very excited for the students who are now in the middle of the implementation stage for their final projects, which in addition to academic projects include industry partnerships with companies like Ebay, Starbucks, Nationwide Insurance, Deloitte, and many others. Can’t wait for the final project presentations in the beginning of May! Based on the most recent team status updates on their final projects, we will see some great work there.