Importance of Markov Chains over Machine Learning

  Markov chains are a fairly common, and relatively simple, way to statistically model random processes. They have been used in many different domains, ranging from text generation to financial modeling. A popular example is r/SubredditSimulator, which uses Markov chains to automate the creation of content for an entire subreddit. Overall, Markov Chains are conceptually quite intuitive, and are very accessible in that they can be implemented without the use of any advanced statistical or mathematical concepts. They are a great way to start learning about probabilistic modeling and data science techniques.

  The Markov Chain is a model used to describe a sequence of consecutive events where the probability or chance of an event depends only on the event before it.If a sequence of events exhibits the Markov Property of the reliance on the previous state, then the sequence is called ‘Markovian’ in nature.

  For some problems in Reinforcement Learning, the actions performed in a particular state is directly related to the previous state, the actions performed in that state and the rewards that the agent receives upon performing said actions.

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