HTML tutorial
CSS3 tutorial
Bootstrap tutorial
JavaScript tutorial
JQuery tutorial
AngularJS tutorial
React tutorial
NodeJS tutorial
PHP tutorial
Python tutorial
Python3 tutorial
Django tutorial
Linux tutorial
Docker tutorial
Ruby tutorial
Java tutorial
C tutorial
C ++ tutorial
Perl tutorial
JSP tutorial
Lua tutorial
Scala tutorial
Go tutorial
ASP.NET tutorial
C # tutorial
Binomial Distribution is a Discrete Distribution
Binomial Distribution is a Discrete Distribution.
It describes the outcome of binary scenarios, e.g. toss of a coin, it will either be head or tails.
It has three parameters:
n
- number of trials.
p
- probability of occurence of each trial (e.g. for toss of a coin 0.5 each).
size
- The shape of the returned array.
Discrete Distribution:The distribution is defined at separate set of events, e.g. a coin toss's result is discrete as it can be only head or tails whereas height of people is continuous as it can be 170, 170.1, 170.11 and so on.
Given 10 trials for coin toss generate 10 data points:
from numpy import random
x = random.binomial(n=10, p=0.5, size=10)
print(x)
from numpy import random
import matplotlib.pyplot as plt
import seaborn as sns
sns.distplot(random.binomial(n=10, p=0.5, size=1000), hist=True, kde=False)
plt.show()
The main difference is that normal distribution is continous whereas binomial is discrete, but if there are enough data points it will be quite similar to normal distribution with certain loc and scale.
from numpy import random
import matplotlib.pyplot as plt
import seaborn as sns
sns.distplot(random.normal(loc=50, scale=5, size=1000), hist=False,
label='normal')
sns.distplot(random.binomial(n=100, p=0.5, size=1000), hist=False,
label='binomial')
plt.show()