Guest speaker Jason Wagoner, a Junior Laufer Fellow from the Laufer Center at Stony Brook, will be speaking.Although his expertise comes largely from molecular simulation, powerful algorithms that can be used in more general statistical settings will be discussed, so if you can, be sure to attend!
Some tricks to reduce correlation times in GHMC simulations
The distribution sampled by a molecular dynamics algorithm is subject to some amount of error that depends on the size of the integration timestep. This error can be corrected by updating the system with a Metropolis Monte Carlo criterion, where the integration step is treated as a selection probability for the candidate state. These methods, closely related to generalized hybrid Monte Carlo (GHMC), satisfy detailed balance by imposing momenta reversal upon candidate rejection. Unfortunately, these momentum reversals can severely increase the time needed for decorrelation, sometimes giving an order-of-magnitude increase in correlation times for system variables. Here, I present the reduced-flipping GHMC algorithm. The algorithm rigorously samples the target distribution but breaks detailed balance to reduce the number of momentum flips in the GHMC simulation. I will also present similar methods in the field that have the same goal to reduce correlation times--extra-chance GHMC and look-ahead GHMC.