# Can anyone help to explain one of the variables in a figure that illustrates how posterior probabilities shift and move around?

I am learning this post.

The book gives this figure to illustrate how posterior probabilities shift and move around Here is the code

%matplotlib inline
from IPython.core.pylabtools import figsize
import numpy as np
from matplotlib import pyplot as plt
figsize(11, 9)

import scipy.stats as stats

dist = stats.beta
n_trials = [0, 1, 2, 3, 4, 5, 8, 15, 50, 500]
data = stats.bernoulli.rvs(0.5, size=n_trials[-1])
x = np.linspace(0, 1, 100)

# For the already prepared, I'm using Binomial's conj. prior.
for k, N in enumerate(n_trials):
sx = plt.subplot(len(n_trials) / 2, 2, k + 1)
plt.xlabel("$$p$$, probability of heads") \
if k in [0, len(n_trials) - 1] else None
plt.setp(sx.get_yticklabels(), visible=False)
plt.fill_between(x, 0, y, color="#348ABD", alpha=0.4)
plt.vlines(0.5, 0, 4, color="k", linestyles="--", lw=1)

leg = plt.legend()
leg.get_frame().set_alpha(0.4)
plt.autoscale(tight=True)

plt.suptitle("Bayesian updating of posterior probabilities",
y=1.02,
fontsize=14)

plt.tight_layout()


Each element in the n_trials list indicates how many times a coin has been tossed.

data represents the records of the trials of tossing a coin.

what does this line mean?

x = np.linspace(0, 1, 100)

• I believe it creates a linear set of values starting with the first argument, 0, finishing with the second argument, 1, and creating 100-2 = 98 linearly spaced values in between. In this case x would be [ 0 0.0101 0.0202 0.0303 ... 0.9899 1 ]. It is used in this example as the x-axis. Jul 18, 2019 at 16:42 