Introduction to Markov Chains

Observe how in the example, the probability distribution is obtained solely by observing transitions from the current day to the next. This illustrates the Markov property, the unique characteristic of Markov processes that renders them memoryless. This typically leaves them unable to successfully produce sequences in which some underlying trend would be expected to occur. For example, while a Markov chain may be able to mimic the writing style of an author based on word frequencies, it would be unable to produce text that contains deep meaning or thematic significance since these are developed over much longer sequences of text. They therefore lack the ability to produce context-dependent content since they cannot take into account the full chain of prior states.