Зміст курсу

Теорія ймовірностей

1. Ознайомемося з основними правилами

Випадкова змінна Схема Бернуллі 1/2 Схема Бернуллі 2/2 Біноміальна ймовірність 1/2 Біноміальна ймовірність 2/2

2. Ймовірності декількох подій

Додавання ймовірностей Обчислення зв'язаних ймовірностей Виклик Перемножуємо ймовірності Залежні ймовірності Закон повної ймовірності Теорема Байєса Виклик

3. Проводимо захоплюючі експерименти

Перший експеримент Другий експеримент Третій експеримент Поєднуйте свої знання

4. Дискретні розподіли

Дискретний рівномірний розподіл Обчислити захоплюючу ймовірність Розподіл Бернуллі Біноміальний розподіл Виклик Пуассонівський розподіл Виклик

5. Нормальний розподіл

Нормальний розподіл Виклик 1 Виклик 2

Біноміальна ймовірність 2/2

Look at the code example of the binomial probability

Explanation of the code above:

We need to import binom object from scipy.stats.
binom.rvs(p = 0.5, size = 5, n = 3) means that the probability of getting head is 50 %, p = 0.5; the size of sample in experiment is 5, size = 5; the number of trial is 3, n = 3.
In the output we can see an array with five results for each coin with the number of successful trials for each coin.

Завдання

Your task here is almost the same as in the previous chapter, play with one coin!

Imagine that here you have a coin with a general probability of 50%. Follow this algorithm:

Import the binom object from scipy.stats.
Conduct the experiment with binom object using rvs() function:
- Set p parameter equal to 0.5.
- Set size parameter equal to 1.
- Set n parameter equal to 5.

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

Завдання

Your task here is almost the same as in the previous chapter, play with one coin!

Imagine that here you have a coin with a general probability of 50%. Follow this algorithm:

Import the binom object from scipy.stats.
Conduct the experiment with binom object using rvs() function:
- Set p parameter equal to 0.5.
- Set size parameter equal to 1.
- Set n parameter equal to 5.

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

Note
Explanation of the output : We were tossing one coin five times, and it only led to success in three cases.

Все було зрозуміло?

Секція 1. Розділ 5

Біноміальна ймовірність 2/2

Look at the code example of the binomial probability

Explanation of the code above:

We need to import binom object from scipy.stats.
binom.rvs(p = 0.5, size = 5, n = 3) means that the probability of getting head is 50 %, p = 0.5; the size of sample in experiment is 5, size = 5; the number of trial is 3, n = 3.
In the output we can see an array with five results for each coin with the number of successful trials for each coin.

Завдання

Your task here is almost the same as in the previous chapter, play with one coin!

Imagine that here you have a coin with a general probability of 50%. Follow this algorithm:

Import the binom object from scipy.stats.
Conduct the experiment with binom object using rvs() function:
- Set p parameter equal to 0.5.
- Set size parameter equal to 1.
- Set n parameter equal to 5.

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

Завдання

Your task here is almost the same as in the previous chapter, play with one coin!

Imagine that here you have a coin with a general probability of 50%. Follow this algorithm:

Import the binom object from scipy.stats.
Conduct the experiment with binom object using rvs() function:
- Set p parameter equal to 0.5.
- Set size parameter equal to 1.
- Set n parameter equal to 5.

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

Note
Explanation of the output : We were tossing one coin five times, and it only led to success in three cases.

Все було зрозуміло?

Секція 1. Розділ 5

Біноміальна ймовірність 2/2

Look at the code example of the binomial probability

Explanation of the code above:

We need to import binom object from scipy.stats.
binom.rvs(p = 0.5, size = 5, n = 3) means that the probability of getting head is 50 %, p = 0.5; the size of sample in experiment is 5, size = 5; the number of trial is 3, n = 3.
In the output we can see an array with five results for each coin with the number of successful trials for each coin.

Завдання

Your task here is almost the same as in the previous chapter, play with one coin!

Imagine that here you have a coin with a general probability of 50%. Follow this algorithm:

Import the binom object from scipy.stats.
Conduct the experiment with binom object using rvs() function:
- Set p parameter equal to 0.5.
- Set size parameter equal to 1.
- Set n parameter equal to 5.

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

Завдання

Your task here is almost the same as in the previous chapter, play with one coin!

Imagine that here you have a coin with a general probability of 50%. Follow this algorithm:

Import the binom object from scipy.stats.
Conduct the experiment with binom object using rvs() function:
- Set p parameter equal to 0.5.
- Set size parameter equal to 1.
- Set n parameter equal to 5.

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

Note
Explanation of the output : We were tossing one coin five times, and it only led to success in three cases.

Все було зрозуміло?

Look at the code example of the binomial probability

Explanation of the code above:

We need to import binom object from scipy.stats.
binom.rvs(p = 0.5, size = 5, n = 3) means that the probability of getting head is 50 %, p = 0.5; the size of sample in experiment is 5, size = 5; the number of trial is 3, n = 3.
In the output we can see an array with five results for each coin with the number of successful trials for each coin.

Завдання

Your task here is almost the same as in the previous chapter, play with one coin!

Imagine that here you have a coin with a general probability of 50%. Follow this algorithm:

Import the binom object from scipy.stats.
Conduct the experiment with binom object using rvs() function:
- Set p parameter equal to 0.5.
- Set size parameter equal to 1.
- Set n parameter equal to 5.

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

Note
Explanation of the output : We were tossing one coin five times, and it only led to success in three cases.

Секція 1. Розділ 5

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