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# Hidden Markov Models - AUusers-cs.au.dk/cstorm/courses/PRiB_f12/slides/hidden-markov-model… ·...

Date post: 15-Feb-2018
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Hidden Markov Models Terminology and Basic Algorithms
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• Hidden Markov ModelsTerminology and Basic Algorithms

• MotivationWe make predictions based on models of observed data (machine learning). A simple model is that observations are assumed to be independent and identically distributed (iid) ...

but this assumption is not always the best, fx (1) measurements of weather patterns, (2) daily values of stocks, (3) acoustic features in successive time frames used for speech recognition, (4) the composition of texts, (5) the composition of DNA, or ...

• Markov ModelsIf the n'th observation in a chain of observations is influenced only by the n-1'th observation, i.e.

If the distributions p(xn | xn-1) are the same for all n, then the chain of observations is an homogeneous 1st-order Markov chain ...

then the chain of observations is a 1st-order Markov chain, and the joint-probability of a sequence of N observations is

• Markov Models

then the chain of observations is a 1st-order Markov chain, and the joint-probability of a sequence of N observations is

If the n'th observation in a chain of observations is influenced only by the n-1'th observation, i.e.

A sequence of observations:The model, i.e. p(xn | xn-1):

If the distributions p(xn | xn-1) are the same for all n, then the chain of observations is an homogeneous 1st-order Markov chain ...

• If the distributions p(xn | xn-1) are the same for all n, then the chain of observations is an homogeneous 1st-order Markov chain ...

Markov Models

then the chain of observations is a 1st-order Markov chain, and the joint-probability of a sequence of N observations is

If the n'th observation in a chain of observations is influenced only by the n-1'th observation, i.e.

A sequence of observations:The model, i.e. p(xn | xn-1):

Extension A higher order Markov chain

• Hidden Markov ModelsWhat if the n'th observation in a chain of observations is influenced by a corresponding latent (i.e. hidden) variable?

If the latent variables are discrete and form a Markov chain, then it is a hidden Markov model (HMM)

H H L L H

Observations

Latent values

• Hidden Markov Models

Markov Model

Hidden Markov Model

If the latent variables are discrete and form a Markov chain, then it is a hidden Markov model (HMM)

H H L L H

Observations

Latent values

What if the n'th observation in a chain of observations is influenced by a corresponding latent (i.e. hidden) variable?

• Hidden Markov Models

Markov Model

Hidden Markov Model

If the latent variables are discrete and form a Markov chain, then it is a hidden Markov model (HMM)

H H L L H

Observations

Latent values

What if the n'th observation in a chain of observations is influenced by a corresponding latent (i.e. hidden) variable?

Computational problems

Determine the likelihood of the sequence of observations

Predict the next observation in the sequence of observations

Find the most likely underlying explanation of the sequence of observation

• What if the n'th observation in a chain of observations is influenced by a corresponding latent (i.e. hidden) variable?

H H L L H

Observations

Latent values

Hidden Markov Models

Markov Model

Hidden Markov Model

If the latent variables are discrete and form a Markov chain, then it is a hidden Markov model (HMM)

The predictive distribution

p(xn+1 | x1,...,xn)

for observation xn+1 can be shown to depend on all previous observations, i.e. the sequence of observations is not a Markov chain of any order ...

• Hidden Markov ModelsWhat if the n'th observation in a chain of observations is influenced by a corresponding latent variable?

Markov Model

Hidden Markov ModelH H L L H

Observations

Latent values

If the latent variables are discrete and form a Markov chain, then it is a hidden Markov model (HMM)

The joint distribution

• Hidden Markov ModelsWhat if the n'th observation in a chain of observations is influenced by a corresponding latent variable?

Markov Model

Hidden Markov ModelH H L L H

Observations

Latent values

If the latent variables are discrete and form a Markov chain, then it is a hidden Markov model (HMM)

The joint distributionTransition probabilities

Emission probabilities

• Transition probabilitiesNotation: In Bishop, the latent variables zn are discrete variables, e.g. if zn = (0,0,1) then the model in step n is in state k=3 ...

Transition probabilities: If the latent variables are discrete with K states, the conditional distribution p(zn | zn-1) is a K x K table A, and the marginal distribution p(z1) describing the initial state is a K vector ...

The probability of going from state j to state k is:

The probability of state k being the initial state is:

• Transition probabilitiesNotation: In Bishop, the latent variables zn are discrete variables, e.g. if zn = (0,0,1) then the model in step n is in state k=3 ...

Transition probabilities: If the latent variables are discrete with K states, the conditional distribution p(zn | zn-1) is a K x K table A, and the marginal distribution p(z1) describing the initial state is a K vector ...

The probability of going from state j to state k is:

The probability of state k being the initial state is:

• Transition ProbabilitiesNotation: The latent variables zn are discrete multinomial variables, e.g. if zn = (0,0,1) then the model in step n is in state k=3 ...

Transition probabilities: If the latent variables are discrete with K states, the conditional distribution p(zn | zn-1) is a K x K table A, and the marginal distribution p(z1) describing the initial state is a K vector ...

The probability of going from state j to state k is:

The probability of state k being the initial state is:

The transition probabilities:

• Transition ProbabilitiesNotation: The latent variables zn are discrete multinomial variables, e.g. if zn = (0,0,1) then the model in step n is in state k=3 ...

Transition probabilities: If the latent variables are discrete with K states, the conditional distribution p(zn | zn-1) is a K x K table A, and the marginal distribution p(z1) describing the initial state is a K vector ...

State transition diagram

The probability of going from state j to state k is:

The probability of state k being the initial state is:

• Emission probabilitiesEmission probabilities: The conditional distributions of the observed variables p(xn | zn) from a specific state

If the observed values xn are discrete (e.g. D symbols), the emission probabilities is a KxD table of probabilities which for each of the K states specifies the probability of emitting each observable ...

• Emission probabilitiesEmission probabilities: The conditional distributions of the observed variables p(xn | zn) from a specific state

If the observed values xn are discrete (e.g. D symbols), the emission probabilities is a KxD table of probabilities which for each of the K states specifies the probability of emitting each observable ...

• Emission probabilitiesEmission probabilities: The conditional distributions of the observed variables p(xn | zn) from a specific state

If the observed values xn are discrete (e.g. D symbols), the emission probabilities is a KxD table of probabilities which for each of the K states specifies the probability of emitting each observable ...

znk = 1 iff the n'th latent variable in the sequence is in state k, otherwise it is 0, i.e. the product just picks the emission probabilities corresponding to state k ...

• HMM joint probability distribution

If A and are the same for all n then the HMM is homogeneous

Observables: Latent states: Model parameters:

• HMM joint probability distribution

If A and are the same for all n then the HMM is homogeneous

Observables: Latent states: Model parameters:

• HMMs as a generative model

Model M: A run follows a sequence of states:

H H L L H

And emits a sequence of symbols:

A HMM generates a sequence of observables by moving from latent state to latent state according to the transition probabilities and emitting an observable (from a discrete set of observables, i.e. a finite alphabet) from each latent state visited according to the emission probabilities of the state ...

• HMMs as a generative model

End

A special End-state can be added to generate finite output

Model M: A run follows a sequence of states:

H H L L H

And emits a sequence of symbols:

A HMM generates a sequence of observables by moving from latent state to latent state according to the transition probabilities and emitting an observable (from a discrete set of observables, i.e. a finite alphabet) from each latent state visited according to the emission probabilities of the state ...

• Using HMMs

Determine the likelihood of the sequence of observations

Predict the next observation in the sequence of observations

Find the most likely underlying explanation of the sequence of observation

• Using HMMs

Determine the likelihood of the sequence of observations

Predict the next observation in the sequence of observations

Find the most likely underlying explanation of the sequence of observation

• Using HMMs

Determine the likelihood of the sequence of observations

Predict the next observation in the sequence of observations

Find the most likely underlying explanation of the sequence of observation

The sum has KN terms, but it can be computed in O(K2N) time ...

• The forward-backward algorithm(zn) is the joint probability of observing x1,...,xn and being in state zn

(zn) is the conditional probability of future observation xn+1,...,xN assuming being in state zn

• (zn) is the joint probability of observing x1,...,xn and being in state zn

(zn) is the conditional probability of future observation xn+1,...,xN assuming being in state zn

Using (zn) and (zn) we get the likelihood of the observations as:

The forward-backward algorithm

• The forward algorithm(zn) is the joint probability of observing x1,...,xn and being in state zn

• The -recursion

• The -recursion

• The forward algorithm(zn) is the joint probability of observing x1,...,xn and being in state zn

Recursion:

• The forward algorithm(zn) is the joint probability of observing x1,...,xn and being in state zn

Recursion:

Basis:

• The forward algorithm(zn) is the joint probability of observing x1,...,xn and being in state zn

Recursion:

Basis:

Takes time O(K2N) and space O(KN) using memorization

• The backward algorithm(zn) is the conditional probability of future observation xn+1,...,xN assuming being in state zn

• The -recursion

• The -recursion

• The backward algorithm

Recursion:

(zn) is the conditional probability of future observation xn+1,...,xN assuming being in state zn

• The backward algorithm

Recursion:

Basis:

(zn) is the conditional probability of future observation xn+1,...,xN assuming being in state zn

• The backward algorithm(zn) is the conditional probability of future observation xn+1,...,xN assuming being in state zn

Recursion:

Basis:

Takes time O(K2N) and space O(KN) using memorization

• Determine the likelihood of the sequence of observations

Predict the next observation in the sequence of observations

Find the most likely underlying explanation of the sequence of observation

Using HMMs

• Predicting the next observation

• Predicting the next observation

• Predicting the next observation

• Determine the likelihood of the sequence of observations

Predict the next observation in the sequence of observations

Find the most likely underlying explanation of the sequence of observation

Using HMMs

The Viterbi algorithm: Finds the most probable sequence of states generating the observations ...

• The Viterbi algorithm(zn) is the probability of the most likely sequence of states z1,...,zn generating the observations x1,...,xn

h

Intuition: Find the longest path from column 1 to column n, where length is its total probability, i.e. the probability of the transitions and emissions along the path ...

• The Viterbi algorithm(zn) is the probability of the most likely sequence of states z1,...,zn generating the observations x1,...,xn

Recursion:

• The Viterbi algorithm(zn) is the probability of the most likely sequence of states z1,...,zn generating the observations x1,...,xn

Recursion:

Basis:

Takes time O(K2N) and space O(KN) using memorization

• (zn) is the probability of the most likely sequence of states z1,...,zn generating the observations x1,...,xn

Recursion:

Basis:

Takes time O(K2N) and space O(KN) using memorization

The path itself can be retrieve in time O(KN) by backtracking

The Viterbi algorithm

• Summary Introduced hidden Markov models (HMMs)

The forward-backward algorithms for determining the likelihood of a sequence of observations, and predicting the next observation in a sequence of observations.

The Viterbi-algorithm for finding the most likely underlying explanation (sequence of latent states) of a sequence of observation

Next: How to implement the basic algorihtms (forward, backward, and Viterbi) in a numerically sound manner.

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