Let's consider this directed graph as a representation of weather conditions, where **S stands for Sunny, C for Cloudy, and R for Rainy**. This Markov chain model helps predict the weather based on the current condition. In this model, transitions occur between states, and each transition has an associated probability.

Imagine we start with a sunny day (S). According to the diagram, there's a 60% chance that the next day will also be sunny, a 30% chance it will be cloudy, and a 10% chance it will rain.

This model provides a simple yet powerful way to predict the weather for the next day based solely on the current day's weather, without needing to consider the sequence of previous days. For example, if today is sunny, we can expect there's a higher likelihood of another sunny day tomorrow, but there’s also a chance of cloudiness or rain, as indicated by the transition probabilities.