 
Summary: Bioinformatics
Hidden Markov Models
Markov Random Processes
A random sequence has the Markov property if its
distribution is determined solely by its current state. Any
random process having this property is called a Markov
random process.
For observable state sequences (state is known from
data), this leads to a Markov chain model.
For nonobservable states, this leads to a Hidden Markov
Model (HMM).
The casino models
Dishonest casino: it has two dice:
Fair die: P(1) = P(2) = P(3) = P(5) =P(6) = 1/6
Loaded die: P(1) = P(2) = ... = P(5) = 1/10 P(6) = 1/2
Casino player approximately switches back&forth between
fair and loaded die once every 20 turns
Game:
You bet $1
You roll (always with a fair die)
