Markov Property

Markov Property is a fundamental concept in probability theory and statistics that describes the memoryless property of a stochastic process. It states that the future state of a system depends only on its present state and not on its past states. In other words, the probability distribution of the future state of the system depends only on the current state of the system and not on any previous states. This property is widely used in various fields, including physics, economics, finance, and computer science, especially in the field of machine learning and artificial intelligence, where it is used to model sequential data such as natural language processing, speech recognition, and image processing.
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