# Quickstart¶

The easiest way to get started with using `emcee`

is to use it for a
project. To get you started, here’s an annotated, fully-functional
example that demonstrates a standard usage pattern.

## How to sample a multi-dimensional Gaussian¶

We’re going to demonstrate how you might draw samples from the multivariate Gaussian density given by:

where \(\vec{\mu}\) is an *N*-dimensional vector position of
the mean of the density and \(\Sigma\) is the square *N*-by-*N*
covariance matrix.

The first thing that we need to do is import the necessary modules:

```
import numpy as np
import emcee
```

Then, we’ll code up a Python function that returns the density
\(p(\vec{x})\) for specific values of \(\vec{x}\),
\(\vec{\mu}\) and \(\Sigma^{-1}\). In fact,
`emcee`

actually requires the logarithm of *p*. We’ll call it `lnprob`

:

```
def lnprob(x, mu, icov):
diff = x-mu
return -np.dot(diff,np.dot(icov,diff))/2.0
```

It is important that the first argument of the probability function is
the position of a single walker (a *N* dimensional
`numpy`

array). The following arguments are going to be constant every
time the function is called and the values come from the `args`

parameter
of our `EnsembleSampler`

that we’ll see soon.

Now, we’ll set up the specific values of those “hyperparameters” in 50 dimensions:

```
ndim = 50
means = np.random.rand(ndim)
cov = 0.5 - np.random.rand(ndim ** 2).reshape((ndim, ndim))
cov = np.triu(cov)
cov += cov.T - np.diag(cov.diagonal())
cov = np.dot(cov,cov)
```

and where `cov`

is \(\Sigma\). Before going on, let’s
compute the inverse of `cov`

because that’s what we need in our
probability function:

```
icov = np.linalg.inv(cov)
```

It’s probably overkill this time but how about we use 250 walkers? Before we go on, we need to guess a starting point for each of the 250 walkers. This position will be a 50-dimensional vector so the initial guess should be a 250-by-50 array—or a list of 250 arrays that each have 50 elements. It’s not a very good guess but we’ll just guess a random number between 0 and 1 for each component:

```
nwalkers = 250
p0 = np.random.rand(ndim * nwalkers).reshape((nwalkers, ndim))
```

Now that we’ve gotten past all the bookkeeping stuff, we can move on to
the fun stuff. The main interface provided by `emcee`

is the
`EnsembleSampler`

object so let’s get ourselves one of those:

```
sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob, args=[means, icov])
```

Remember how our function `lnprob`

required two extra arguments when it
was called? By setting up our sampler with the `args`

argument, we’re
saying that the probability function should be called as:

```
lnprob(p, means, icov)
```

where `p`

is the position of a single walker. If we didn’t provide any
`args`

parameter, the calling sequence would be `lnprob(p)`

instead.

It’s generally a good idea to run a few “burn-in” steps in your MCMC
chain to let the walkers explore the parameter space a bit and get
settled into the maximum of the density. We’ll run a burn-in of 100
steps (yep, I just made that number up… it’s hard to really know
how many steps of burn-in you’ll need before you start) starting from
our initial guess `p0`

:

```
pos, prob, state = sampler.run_mcmc(p0, 100)
sampler.reset()
```

You’ll notice that I saved the final position of the walkers (after the
100 steps) to a variable called `pos`

. You can check out what will be
contained in the other output variables by looking at the documentation for
the `EnsembleSampler.run_mcmc()`

function. The call to the
`EnsembleSampler.reset()`

method clears all of the important bookkeeping
parameters in the sampler so that we get a fresh start. It also clears the
current positions of the walkers so it’s a good thing that we saved them
first.

Now, we can do our production run of 1000 steps (again, this is probably overkill… it’s generally very silly to take way more samples than you need to but never mind that for now):

```
sampler.run_mcmc(pos, 1000)
```

The sampler now has a property `EnsembleSampler.chain`

that is a
`numpy`

array with the shape `(250, 1000, 50)`

. Take note of that
shape and make sure that you know where each of those numbers come from.
A much more useful object is the `EnsembleSampler.flatchain`

which
has the shape `(250000, 50)`

and contains all the samples reshaped into
a flat list. You can see now that we now have 250 000 unbiased samples of
the density \(p(\vec{x})\). You can make histograms of these
samples to get an estimate of the density that you were sampling:

```
import matplotlib.pyplot as pl
for i in range(ndim):
pl.figure()
pl.hist(sampler.flatchain[:,i], 100, color="k", histtype="step")
pl.title("Dimension {0:d}".format(i))
pl.show()
```

Another good test of whether or not the sampling went well is to check
the mean acceptance fraction of the ensemble using the
`EnsembleSampler.acceptance_fraction()`

property:

```
print("Mean acceptance fraction: {0:.3f}"
.format(np.mean(sampler.acceptance_fraction)))
```

This number should be between approximately 0.25 and 0.5 if everything went as planned.

Well, that’s it for this example. You’ll find the full, unadulterated sample code for this demo here.