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# Fundamentals of Hidden Markov Model

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Fundamentals of Hidden Markov Model Mehmet Yunus Dönmez
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Fundamentals of Hidden Markov Model

Mehmet Yunus Dönmez

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).

HMM Elements

An HMM for discrete symbol observation- N

the number of states in the modelthe state at time t

- Mthe number of distinct observation symbols per state

{1,2,..., }Ntq

1 2{ , ,..., }MV v v v

HMM Elements (2)

- : the state-transition probability distribution : A

- : the observation symbol probability distribution : B

ija

1[ | ]ij t ta p q j q i 1 ,i j N

( )jb k

( ) [ | ]j t k tb k p o v q j 1 k M

HMM Elements (3)

- : the initial state distribution,

Compact Notation of a HMM Model

=

i

1[ ]i p q i 1 i N

( , , )A B

1 1 2 2 ( 1)1 1 2( ) ( )... ( )T T Tq q q q q q q Tb o a b o a b o

1 2 1 2( , ,... | , , ,..., )T q q qTp o o o s s s

A General Case HMM

HMM Generator

Choose an initial state ( ) initial state distributionSet Choose symbol probability distributionTransit to a new state state transition probability distributionSet ; return to step 3 if ;otherwise, terminate the procedure

1q i1t t ko v

1tq j

1t t t T

HMM Properties

Often simplified , and

Obviously for all i

Discrete HMMs :

Continuous HMMs :

1( ) 1s 1( ) 0is

1ijja

1 2{ , ,... }MV v v v

dVR

HMM Properties (2)

The term “hidden” - we can only access to visible symbols (observations)- drawing conclusions without knowing the hidden sequence of states

Causal: Probabilities depend on previous states

Ergodic if every state is visited in transition sequence for any given initial state

Final or absorbing state: the state which, if entered, is never left

3 Basic Problems

The Evaluation Problem- given an HMM - given an observation- compute the probability of the observation

1 2{ , ,... | }Tp o o o

1 2, ,... To o o

3 Basic Problems (2)

The Decoding Problem- given an HMM- given an observation - compute the most likely state sequence

i.e.

1 2, ,... To o o

1 2, ,...,q q qTs s s

1,... 1 2 1argmax ( , ,..., | ,... , )q qT T Tp o o o q q

3 Basic Problems (3)

The learning / optimization problem - given an HMM - given an observation - find an HMM such that

1 2, ,... To o o

1 2 1 1 2{ , ,... | } { , ,... | }T Tp o o o p o o o

The Evaluation Problem

We know :

=

- From this :

=

1 2 1 2( , ,... | , , ,..., )T q q qTp o o o s s s

1 1 1 11 11,... 1( ) ( ) ( )

k k kq q q q q kk Ts b o a b o

1 2( , ,... | )Tp o o o

1 1 1 112

3

1 11,... 1,... 11,...1,...

1,...

( ) ( ) ( )k k k

T

q q q q q kq N k Tq Nq N

q N

s b o a b o

The Evaluation Problem(2)

Obvious:for sufficiently large values of T, it is infeasible to compute the above term for all possible state sequences need other solution

The Forward Algorithm

At time t and state i, probability of partial observation sequence

: array

1 2, ,... to o o ( )t i

1 1( ) ( )i ii b o 1 i N

[ ][ ]time state

1 11

( ) [ ( ) ] ( )N

t t ij j ti

j i a b o

The Forward Algorithm (2)

As a result at the last time T

[ ][ ] ( )timetime state state

[ ][ ]state

T state1 2( , ,... | )Tp o o o

Figure

The Backward Algorithm

1 2

1 11

1 2 11

, ,... ( )

( ) 1

( ) ( ) ( )

1, 2,...1

( , ,... | ) ( )

t t T t

T

N

t ij j t tj

N

Tj

o o o i

i

i a b o j

t T T

p o o o j

Figure

The Decoding Problem

Finding the “optimal” state sequence associated with the given observation sequence

Forward-Backward

Optimality criterion : to choose the states that are individually most likely at each time t

The probability of being in state i at time t

: accounts for partial observation sequence : account for remainder

tq

1

( ) ( | , )

( ) ( )

( ) ( )

i t

t tN

t ti

t p q i O

i i

i i

( )t i( )t i 1 2, ,...t t To o o

1 2, ,... to o o

The Viterbi Algorithm

The best score along a single path, at time t, which accounts for the first t observations and ends in state i

Keep track of the argument that maximize above equation

Viterbi Algorithm is similar in implementation to the forward calculation, but the major difference is the maximization over previous states

1 1( ) [max ( ) ] ( )t t ij j ti

j i a b o

( )t j

The Complete Procedure (for finding the best state sequence)

Initialization

Recursion

1 1

1

( ) ( )

( ) 0i ii b o

i

1 i N

11

11

( ) max[ ( ) ] ( )

( ) argmax[ ( ) ]

t t ij j ti N

t t iji N

j i a b o

j i a

2

1

t T

j N

The Complete Procedure (2)(for finding the best state sequence)

Termination

Path(state sequence) backtracking

*

1

*

1

max[ ( )]

argmax[ ( )]

Ti N

T Ti N

P i

q i

* *1 1( )

1, 2,...,1t t tq q

t T T

The Learning / Optimization problem

How do we adjust the model parameters to maximize ??

- Parameter Estimation- Baum-Welch Algorithm ( EM : Expectation

Maximization )- Iterative Procedure

( | )P O

Parameter Estimation

Probability of being in state i at time t, and state j at time t+1

1

1 1

1 11 1

( , ) ( , | , )

( ) ( ) ( )

( ) ( ) ( )

t t t

t ij j t t

N N

t ij j t ti j

i j P q i q j O

i a b o j

i a b o j

Figure

Parameter Estimation (2)

Probability of being in state i at time t, given the entire observation sequence and the model

We can relate these by summing over j

1

( ) ( , )N

t tj

i i j

Parameter Estimation (3)

By summing over time index t …- expected number of times that state i visited- expected number of transitions made from state i

• That is …

= expected number of times that state i in O

= expected number of transitions made from state i to j in O

1

1

( )T

tt

i

1

1

( , )T

tt

i j

Parameter Estimation (4)

Update using &

: expected frequency (number of times) in state i at time (t=1)

( , , )A B ( , )t i j ( )i t

_

1( )i i

Parameter Estimation (5)

New Transition Probability …

expected number of transitions from state i to j

expected number of transitions from state I

=

1

_11

1

( , )

( )

T

tt

ij T

tt

i ja

i

Parameter Estimation (6)

New Observation Probability…

expected number of times in state j and observing symbol

expected number of times in j

=

kv

1_. .

1

( )

( )( )

t k

T

tts t o v

j T

tt

j

b kj

Parameter Estimation (7)

From , if we define new

- New model is more likely than old model in the sense that

- The observation sequence is more likely to be produced by new model- has been proved by Baum & his colleagues- iteratively use new model in place of old model, and repeat the reestimation calculation “ML estimation”

( , , )A B _ _ _ _

( , , )A B

_

( | ) ( | )P O P O

Questions??

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