import numpy
import matplotlib.pyplot as plt

theta = 0.2
n = 100
for i in range(2,8):
    sample = 5 ** i
    most_likely = numpy.random.binomial(n,theta,sample)/float(n)
    plt.hist(most_likely,bins=99,range=[0,1])
    plt.title("sample = %d" % sample)
    plt.savefig("%d.eps" % sample)
    plt.close()

ab903240d4e62be7788983ea6c85f7df534e643f
<mask token>

<mask token>
for i in range(2, 8):
    sample = 5 ** i
    most_likely = numpy.random.binomial(n, theta, sample) / float(n)
    plt.hist(most_likely, bins=99, range=[0, 1])
    plt.title('sample = %d' % sample)
    plt.savefig('%d.eps' % sample)
    plt.close()

<mask token>
theta = 0.2
n = 100
for i in range(2, 8):
    sample = 5 ** i
    most_likely = numpy.random.binomial(n, theta, sample) / float(n)
    plt.hist(most_likely, bins=99, range=[0, 1])
    plt.title('sample = %d' % sample)
    plt.savefig('%d.eps' % sample)
    plt.close()

import numpy
import matplotlib.pyplot as plt
theta = 0.2
n = 100
for i in range(2, 8):
    sample = 5 ** i
    most_likely = numpy.random.binomial(n, theta, sample) / float(n)
    plt.hist(most_likely, bins=99, range=[0, 1])
    plt.title('sample = %d' % sample)
    plt.savefig('%d.eps' % sample)
    plt.close()

import numpy
import matplotlib.pyplot as plt

theta = 0.2
n = 100
for i in range(2,8):
    sample = 5 ** i
    most_likely = numpy.random.binomial(n,theta,sample)/float(n)
    plt.hist(most_likely,bins=99,range=[0,1])
    plt.title("sample = %d" % sample)
    plt.savefig("%d.eps" % sample)
    plt.close()
