Markov Assumptions
Let X=(X1, .., Xt) be a sequence of random variables taking values in some finite set S={s1, …, sn}, the state space, the Markov properties are:
Limited Horizon: P(Xt+1=sk|X1, .., Xt)=P(X t+1 = sk |Xt) i.e., a word’s tag only depends on the previous tag.
Time Invariant: P(Xt+1=sk|X1, .., Xt)=P(X2 =sk|X1) i.e., the dependency does not change over time.
If X possesses these properties, then X is said to be a Markov Chain