# FAQ

## Contents

# FAQ#

**The not-so-frequently asked questions that still have useful answers**

## What are “walkers”?#

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.

## How should I initialize the walkers?#

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.

## Parameter limits#

In order to confine the walkers to a finite volume of the parameter space, have your function return negative infinity outside of the volume corresponding to the logarithm of 0 prior probability using

```
return -numpy.inf
```