convergence_criterion masks and thins a set of MCMC chains

...rather than taking a list of arrays as an arg, it now takes
a list of MCMC objects and does the masking/thinning itself.
Added inverse covariance function
First attempt at adaptive hessian guided MCMC

* Use the sigma parameter to scale the size of the jumpbs during
  hessian phase, not just during the annealing phase
* Adjust sigma up and down by multiplying or dividing by a scaling
  factor, rather than just be incrementing or decrementing
* Decide whether to adjust sigma based on the "local" acceptance
  rate, i.e., the number of accepts within a recent window of steps.
for plot.surf, make surface edges visible (slightly)
cleaned up some hessian calculation bits
File showing how to set up a lognormal variable in PyMC
A simple didactic example of how to use PyMC to fit a PySB model

Uses a simple exponential decay model written in PySB.
PyMC version of fitting the robertson model
Version of decay model with explicit calculation of the likelihood
removed unnecessary field initialization from __init__
added several options to plot.surface

walk, step, bounds0/1, zmax, parallelize
Merge remote-tracking branch 'jmuhlich/master'
initialized hessian to None in initialize
Merge remote-tracking branch 'jmuhlich/master'
Made the accept_window a field in mcmc.options
Fixed CC calculation to use only the accepted moves in calculation
Fixed bug in convergence calculation
Helper method to return the thinned, mixed accepts
Report module with MCMC diagnostic tests
A prune function for keeping only the mixed accepted steps