This article explains the difference between continuous and discrete probability distributions, introduces common examples such as Bernoulli, Binomial, Poisson, Uniform, Exponential, and Normal distributions, and describes how these distributions relate to each other.
This article introduces the Bernoulli, Binomial, Poisson, and Exponential probability distributions, detailing their definitions, key properties, and real‑world examples such as lottery draws, loan‑approval counts, rare market events, and inventory control, while illustrating how the Binomial converges to the Poisson and linking to the Exponential inter‑arrival times.
This article demonstrates how to use Python to simulate coin tosses, construct the likelihood function for a Bernoulli distribution, and apply maximum likelihood estimation to recover the underlying head probability, illustrating the method with code, equations, and visualizations.
