old_contents: from pymc import *
  import numpy as np 
  import theano 
  
  #import pydevd 
  #pydevd.set_pm_excepthook()
  np.seterr(invalid = 'raise')
  
  data = np.random.normal(size = (2, 20))
  
  
  model = Model()
  
  with model:
      x = Normal('x', mu = .5, tau = 2.**-2, shape = (2,1))
  
      z = Beta('z', alpha = 10, beta =5.5)
  
      d = Normal('data', mu = x, tau = .75**-2, observed = data)
  
      start = model.test_point
  
      h = approx_hess(start)
      step = HamiltonianMC(model.vars, h)
  
      trace = sample(1e3, step, start)
  
  from pylab import * 
  subplot(2,2,1)
  plot(trace[x][:,0,0])
  subplot(2,2,2)
  hist(trace[x][:,0,0])
  
  subplot(2,2,3)
  plot(trace[x])
  subplot(2,2,4)
  hist(trace[x][:,0,0])
  show()
  
new_contents: from pylab import * 
  from pymc import *
  import numpy as np 
  import theano 
  
  #import pydevd 
  #pydevd.set_pm_excepthook()
  np.seterr(invalid = 'raise')
  
  data = np.random.normal(size = (2, 20))
  
  
  model = Model()
  
  with model:
      x = Normal('x', mu = .5, tau = 2.**-2, shape = (2,1))
  
      z = Beta('z', alpha = 10, beta =5.5)
  
      d = Normal('data', mu = x, tau = .75**-2, observed = data)
  
      start = model.test_point
  
      h = approx_hess(start)
      step = HamiltonianMC(model.vars, h)
  
      trace = sample(1e3, step, start)
  
  subplot(2,2,1)
  plot(trace[x][:,0,0])
  subplot(2,2,2)
  hist(trace[x][:,0,0])
  
  subplot(2,2,3)
  plot(trace[x][:,1,0])
  subplot(2,2,4)
  hist(trace[x][:,1,0])
  show()
  
subject: fix plot