# MCMC: long burn in vs re-initialization of the chain?

The developer of the well-known emcee package often gives this advice to help with chain convergence:

1. Run a short (few hundred steps) chain
2. Reinitialize all the walkers near the point with maximum log probability seen so far
4. Then run your final chain starting where you ended up for your last run of step 1

My question is: is this any different than simply allowing the chain(s) to run for more steps and then discard a longer burn-in period? I.e., is this any different from a "normal" run with more burn-in steps?

However, it can accelerate convergence if you allow your chains to interact. Start from a random position, and let all your chains run independently for $$T$$ steps. Then, set all the walkers of all the chains to the same position (the point with maximum log probability seen so far across all chains), and let them run again independently. Hence, the chain that happened to be the closest to the high likelihood area will "guide" the others towards this position.