TensorTonic | Bernoulli Probability Mass Function & Moments
Learn how to implement the Bernoulli Probability Mass Function (PMF) and compute its mean and variance using Python and NumPy on TensorTonic.
🕛 Timestamps
00:00 - Problem description
00:55 - Visualization
02:23 - Implementation
In this video, I explain the Bernoulli distribution, where a random variable can take only two possible outcomes: 0 for failure and 1 for success. We also implement the PMF formula and calculate the distribution moments:
P(X = 1) = p
P(X = 0) = 1 - p
Mean = p
Variance = p(1 - p)
This is a beginner-friendly probability and statistics problem that helps build intuition for machine learning, data science, and mathematical foundations.
In this video:
Bernoulli distribution intuition
Probability Mass Function explanation
Mean and variance formula
Python NumPy implementation
Handling scalar, list, and array inputs
Passing TensorTonic test cases
If you are learning machine learning, probability, statistics, or Python coding, this video will help you understand one of the most fundamental distributions used in ML.
#TensorTonic #MachineLearning #Probability #Statistics #Python #NumPy #BernoulliDistribution