The not-so-frequently asked questions that still have useful answers
Walkers are the members of the ensemble. They are almost like separate Metropolis-Hastings chains but, of course, the proposal distribution for a given walker depends on the positions of all the other walkers in the ensemble. See Goodman & Weare (2010) for more details.
The best technique seems to be to start in a small ball around the a priori preferred position. Don’t worry, the walkers quickly branch out and explore the rest of the space.
I’m getting weird spikes in my data/I have low acceptance fractions/both... what should I do?
Double the number of walkers. If that doesn’t work, double it again. And again. Until you run out of RAM. At that point, I don’t know!
The walkers are getting stuck in “islands” of low likelihood. How can I fix that?
Try increasing the number of walkers. If that doesn’t work, you can try pruning using a clustering algorithm like the one found in arxiv:1104.2612.