Model fitting with MCMC in Python:

This notebook illustrates how to use the pymc module to fit a two-component model
to observations of the radial surface brightness profile of M31, as originally
measured by Kent et al., 1987.

Running this code requires the following modules and files to be installed and present:

  • PyLab, Scipy/Numpy etc
  • pymc
  • m31_SB_profile.txt


Import pylab using the IPy Notebook magic inline function (%inline), and import our model
(defined in the file '') as well as the pymc MCMC fitter and plotting function.

In [1]:
%pylab inline
import SB_model
from pymc import MCMC, Matplot
Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].
For more information, type 'help(pylab)'.

Create the MCMC fitter

Here we construct the MCMC object out of our model. Remember that the model
is just a set of interconnected random variables --- the hard work is all handled by this MCMC object.

In [2]:
M = MCMC(SB_model)

Accessing variables

Now that we have our MCMC object, which we called 'M', we can access the model's
variables and their values. For non-observed variables (like SB), a call to .value
returns the variable's current value, while the values of observed variables don't change,
of course. Here we plot our observations in red, and a realization of our model generated
from our priors on the parameters --- i.e. our model before we begin fitting it.

In [3]:
plt.plot(M.r.value, M.SB.value, c='gray', label='model')
plt.scatter(M.r.value, M.mags.value, c='r', label='observations')

plt.xlabel('radius (")')
plt.ylabel('Surface Brightness (mag)')
<matplotlib.legend.Legend at 0x110ca5f50>

Fitting the model

To do this, we sample from the posterior distribution built up by the Markov Chain.
The first keyword, iter, is the number of samples to draw from our posterior.
This is a tradeoff between quality and execution time, since more samples is pretty
much always better.

Of course, we only want to sample after the Markov Chain has found the equilibrium
distribution, i.e. after our sampling has converged to the true posterior. That's
where the burn keyword comes in: the first burn samples will be thrown away.
We want to ensure that burn is large enough so that our Markov Chain converges
to the true distribution after that many samples.

MCMC sampling is oftentimes inherently auto-correlated (the current value of
sampled parameters influences the next sample), so thinning can be helpful.
A sample is collected only every thin steps of the Markov Chain. This gets
our sampling closer to the independently-distributed ideal.

In [4]:
# reasonable values for this model
# this will take a few seconds to run, so be patient!
M.sample(iter=5e4, burn=1000, thin=100)
[****************100%******************]  50000 of 50000 complete

Visualizing the Posterior

The pymc Matplot module is a great way to get a quick snapshot of
the posterior. For each parameter, it produces three plots:

  • A trace, which shows the value of the parameter over time as the Markov Chain walks around.
    This should be very noisy --- if the trace has a lot of clear structure, you probably need
    to increase the number of samples you burn.
  • An autocorrelation plot. High autocorrelation values indicate that you probably should
    increase the thinning parameter, and sample the space more randomly.
  • A histogram, which will approximate the posterior PDF of that parameter.
    This plot is the holy grail of this endeavor --- it shows you the mean (or best-fit)
    value for each parameter, as well as the 95% confidence interval.
In [5]:
# plot function takes the model (or a single parameter) as an argument:
Plotting r_e_B
Plotting r_e_D
Plotting M_e_B
Plotting M_e_D
Plotting n

Making inferences about model parameters

The stats() function provides an interface to the statistics of our posterior,
in the form of a dictionary. For example, let's find the predicted ratio between
effective sizes of the disk and the bulge, and let's also explore how confidently
we can determine the effective surface brightness of the disk.

In [6]:
print 'R_effective (bulge) / R_effective (disk) =', \
       M.stats()['r_e_B']['mean'] / M.stats()['r_e_D']['mean']
print 'Effective surface brightness of the bulge: \n', \
       '    Best-fit value:', M.stats()['M_e_B']['mean'], \
       '\n    95% Confidence interval:', M.stats()['M_e_B']['quantiles'][2.5], \
        'to', M.stats()['M_e_B']['quantiles'][97.5]
R_effective (bulge) / R_effective (disk) = 0.201445991003
Effective surface brightness of the bulge: 
    Best-fit value: 20.7427508071 
    95% Confidence interval: 20.2909794208 to 21.0286373194

Visualizing specific realizations of our model

The trace() method presents the values of a variable for all of the saved
Markov Chain steps. Let's plot up several of these traces, and see how
the model changes with different parameter values.

In [7]:
for i in range(50):
    plt.plot(M.r.value, M.trace('SB')[i], c='gray', alpha=.25)

plt.scatter(M.r.value, M.mags.value, c='r') 
plt.xlabel('radius (")')
plt.ylabel('Surface Brightness (mag)')
<matplotlib.text.Text at 0x1115027d0>

Visualizing the best-fit model and its components

Now that we have our best-fit, let's see how it decomposes into the components,
the bulge and the disk. In other words, we can finally take our results and
explore what they imply about the physical system we observed!

In [8]:
# our best-fit parameters
r_e_B = M.stats()['r_e_B']['mean']
r_e_D = M.stats()['r_e_D']['mean']
M_e_B = M.stats()['M_e_B']['mean']
M_e_D = M.stats()['M_e_D']['mean']
n     = M.stats()['n']['mean']
r     = M.r.value

from SB_model import sersic
# the sersic profile defined in expects brightnesses
#  in flux units, not magnitudes, so we have to convert M_e_B and
#  M_e_D before feeding them in.
plt.plot(r, M.stats()['SB']['mean'], c='k')
plt.plot(r, -2.5*np.log10(sersic( r, r_e_B, n, 10**(-.4*M_e_B) )), 'g:', label='bulge')
plt.plot(r, -2.5*np.log10(sersic( r, r_e_D, 1., 10**(-.4*M_e_D) )), 'g--', label='disk')
plt.scatter(M.r.value, M.mags.value, c='r', label='obs')

plt.axis( [0, 6000, 26, 14] )
plt.ylabel('Surface Brightness (mag)')
plt.xlabel('Radius (")')
<matplotlib.legend.Legend at 0x110ce8f90>