M is the set of cases;
A is the matrix of transition probabilities;
p is the initial probability distribution. If the probability of transition from any step to other steps is zero, this is fixed as a final state. Thus, when the system
enters the
final state, it never moves to the next step.
In a hidden Markov model, the state of the system is hidden (unknown), but at each time step
t, the system in the state
s(t) determines the observed/visible value
v(t). The diagram below shows the general outline of a Hidden Markov Model:
Figure 4. General scheme of Hidden Markov model The following considerations apply to HMM:
-
We can define a certain sequence of visible/observable states as V^T={v(1),v(2)…v(T)}; -
Let's denote our model as θ. Therefore, for any state of s(t) there is a probability of the state vk(t). -
Since we only have visible states, s(t)s is not observable, and such model is called the Hidden Markov model. -
Such network is called the Limited state machine . -
If state machines are associated with transition probabilities, this is called the Markov network . Biz ko‘rinadigan/kuzatiladigan holatlarning ma’lum bir ketma -ketligini sifatida belgilashimiz mumkin; Probability of emission Let's redefine our previous example. Suppose that depending on any 3 states (Q1, Q2, Q3), there are
visible/observable symbols of V1 and V2. An
