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Bioinformatics Hidden Markov Models

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 non-observable 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
You bet $1
You roll (always with a fair die)


Source: Arias, Marta - Departament of Llenguatges i Sistemes Informátics, Universitat Politècnica de Catalunya


Collections: Computer Technologies and Information Sciences