def test_gibbs_chain_burn_in():
start_location = array([2.0, -4.0])
width_guesses = array([5.0, 0.05])
chain = GibbsChain(posterior=rosenbrock,
start=start_location,
widths=width_guesses)
steps = 50
chain.advance(steps)
burn = chain.estimate_burn_in()
assert 0 < burn <= steps
chain.autoselect_burn()
assert chain.burn == burn
# The plot_diagnostics() method can help us decide what size of burn-in to use:
chain.plot_diagnostics()
# Occasionally samples are also 'thinned' by a factor of n (where only every
# n'th sample is used) in order to reduce the size of the data set for
# storage, or to produce uncorrelated samples.
# based on the diagnostics we can choose to manually set a global burn and
# thin value, which is used (unless otherwise specified) by all methods which
# access the samples
chain.burn = 2000
chain.thin = 5
# the burn-in and thinning can also be set automatically as follows:
chain.autoselect_burn()
chain.autoselect_thin()
# After discarding burn-in, what we have left should be a representative
# sample drawn from the posterior. Repeating the previous plot as a
# scatter-plot shows the sample:
p = chain.get_probabilities() # color the points by their probability value
plt.scatter(chain.get_parameter(0), chain.get_parameter(1), c = exp(p-max(p)), marker = '.')
plt.xlabel('parameter 1')
plt.ylabel('parameter 2')
plt.grid()
plt.show()
# We can easily estimate 1D marginal distributions for any parameter
# using the 'get_marginal' method: