Sunny Rainy Source: Markov Model

This lecture introduces the concept of a first-order Markov model using the example of a sunny-rainy source with two states, where the weather on each day depends only on the weather of the previous day. The instructor explains how to calculate probabilities for different weather conditions based on the model's parameters and demonstrates the application of the chain rule to compute joint and conditional entropies. The lecture emphasizes the importance of understanding and utilizing Markov models for predicting and analyzing sequential data.