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Probability Theory

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Bernoulli Trial 2/2

It is time to connect your math knowledge with your programming skills. Look to this example:

# Import relevant libraries
import numpy as np
from scipy.stats import bernoulli

# Here, you simulate an experiment of tossing 5 coins
experiment = bernoulli.rvs(p = 0.5, size = 5)
print(experiment)

              
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# Import relevant libraries 
import numpy as np
from scipy.stats import bernoulli

# Here, you simulate an experiment of tossing 5 coins
experiment = bernoulli.rvs(p = 0.5, size = 5)
print(experiment)
copy

You can treat this function as an actual experiment; whenever you click the run button, the output is different.

Explanation of the code above:

  1. You need to import bernoulli object from scipy.stats. With this object, we will conduct a probability experiment on a computer.
  2. bernoulli.rvs(p = 0.5, size = 5) means that the probability of getting head is 50 %, p = 0.5, the sample size in experiment is 5, size = 5.
  3. The output shows an array with five results for each coin 1 means success and 0 means failure.
  4. [1 1 1 1 0] we had a successful result for 4 coins and failed for the last one.

Note

In this chapter and many other chapters, we will use the np.random.seed() function, do not be petrified it should be written to make your and my outputs equal. Do not change it.

Tarefa

Swipe to start coding

Your task is to play a little bit with the function. Imagine that you have an unbelievable successful coin and in 90% of cases tossing a coin you receive a head. Follow the algorithm to experiment:

  1. Import the bernoulli object from scipy.stats.
  2. Conduct the experiment with bernoulli object using .rvs() method.
    • Set p parameter equal to 0.9.
    • Set size parameter equal to 1.

By the way, you can comment on the line where np.random.seed() was defined and "play with the coin" to receive various outputs.

Solução

# Import relevant libraries
from scipy.stats import bernoulli
import numpy as np
np.random.seed(0)

# Here, you simulate an experiment of tossing 1 coins
experiment = bernoulli.rvs(p = 0.9, size = 1)
print(experiment)

Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 1. Capítulo 3
# Import relevant libraries
___
import numpy as np
np.random.seed(0)

# Here, you simulate an experiment of tossing 1 coin
experiment = ___.___(p = ___, ___)
print(experiment)
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