Torpid mixing of simulated tempering on the Potts model

Simulated tempering and swapping are two families of sampling algorithms in which a parameter representing temperature varies during the simulation. The hope is that this will overcome bottlenecks that cause sampling algorithms to be slow at low temperatures. Madras and Zheng demonstrate that the swapping and tempering algorithms allow efficient sampling from the low-temperature mean-field Ising model, a model of magnetism, and a class of symmetric bimodal distributions [10]. Local Markov chains fail on these distributions due to the existence of bad cuts in the state space. Bad cuts also arise in the

• Type: Conference
• Year: 2004
• Where: SODA
• Authors: Nayantara Bhatnagar, Dana Randall
• Keywords: Algorithm, Algorithms, Ising Model, Potts Model, SODA 2004