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Markov processes

Markov processes are mathematical models that describe systems where the future state depends only on the current state, not on the past states. This property is known as "memorylessness." Imagine playing a board game where your next move depends only on your current position and the rules, not on how you got there. Markov processes are used in various fields, such as finance, weather forecasting, and machine learning, to predict outcomes based on current conditions, making them powerful tools for decision-making and modeling dynamic systems.

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    Markov processes are mathematical models that describe systems where the future state depends only on the current state, not on past states. Imagine a weather forecast: if today is rainy, it affects tomorrow's chances of rain, but yesterday's weather doesn't matter. This property is called "memorylessness." Markov processes are widely used in various fields, including finance, physics, and computer science, to predict outcomes and understand complex systems through transitions between states in a structured way.