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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 MCMC Step Size
01 - Exploratory
Author
Step1:  It's a $50000 \times 6$ dataset.
Step2:  Not bad!  There is little constraint on $\log{g}, Z$, but we sort of expected that.
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Python Code:
import h5py
f = h5py.File('../data/mc.hdf5', mode='r')
list(f.keys())
d = f['samples']
d.value.shape
Explanation: MCMC Step Size
01 - Exploratory
Author: Michael Gully-Santiago
Thursday, January 21, 2016
We want to know more about optimal step sizes.
End of explanation
import matplotlib.pyplot as plt
% matplotlib inline
% config InlineBackend.figure_format = 'retina'
import seaborn as sns
from IPython.display import Image
!chain.py --files ../data/mc.hdf5 --chain --burn=10000
Image(filename='walkers.png')
!chain.py --files ../data/mc.hdf5 --triangle --burn=10000
Image(filename='triangle.png')
Explanation: It's a $50000 \times 6$ dataset.
End of explanation
!chain.py --files ../data/mc.hdf5 --cov
Image(filename='cor_coefficient.png')
import numpy as np
ojs = np.load('opt_jump.npy')
ojs.shape
Explanation: Not bad!  There is little constraint on $\log{g}, Z$, but we sort of expected that.
End of explanation
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