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A *hidden Markov model* (HMM)
is one in which you observe a sequence of emissions, but do not know
the sequence of states the model went through to generate the emissions.
Analyses of hidden Markov models seek to recover the sequence of states
from the observed data.

As an example, consider a Markov model with two states and six possible emissions. The model uses:

A red die, having six sides, labeled 1 through 6.

A green die, having twelve sides, five of which are labeled 2 through 6, while the remaining seven sides are labeled 1.

A weighted red coin, for which the probability of heads is .9 and the probability of tails is .1.

A weighted green coin, for which the probability of heads is .95 and the probability of tails is .05.

The model creates a sequence of numbers from the set {1, 2, 3, 4, 5, 6} with the following rules:

Begin by rolling the red die and writing down the number that comes up, which is the emission.

Toss the red coin and do one of the following:

If the result is heads, roll the red die and write down the result.

If the result is tails, roll the green die and write down the result.

At each subsequent step, you flip the coin that has the same color as the die you rolled in the previous step. If the coin comes up heads, roll the same die as in the previous step. If the coin comes up tails, switch to the other die.

The state diagram for this model has two states, red and green, as shown in the following figure.

You determine the emission from a state by rolling the die with the same color as the state. You determine the transition to the next state by flipping the coin with the same color as the state.

The transition matrix is:

The emissions matrix is:

The model is not hidden because you know the sequence of states from the colors of the coins and dice. Suppose, however, that someone else is generating the emissions without showing you the dice or the coins. All you see is the sequence of emissions. If you start seeing more 1s than other numbers, you might suspect that the model is in the green state, but you cannot be sure because you cannot see the color of the die being rolled.

Hidden Markov models raise the following questions:

Given a sequence of emissions, what is the most likely state path?

Given a sequence of emissions, how can you estimate transition and emission probabilities of the model?

What is the

*forward probability*that the model generates a given sequence?What is the

*posterior probability*that the model is in a particular state at any point in the sequence?

Statistics Toolbox™ functions related to hidden Markov models are:

`hmmgenerate`— Generates a sequence of states and emissions from a Markov model`hmmestimate`— Calculates maximum likelihood estimates of transition and emission probabilities from a sequence of emissions and a known sequence of states`hmmtrain`— Calculates maximum likelihood estimates of transition and emission probabilities from a sequence of emissions`hmmviterbi`— Calculates the most probable state path for a hidden Markov model`hmmdecode`— Calculates the posterior state probabilities of a sequence of emissions

This section shows how to use these functions to analyze hidden Markov models.

The following commands create the transition and emission matrices for the model described in the Introduction to Hidden Markov Models (HMM):

TRANS = [.9 .1; .05 .95;]; EMIS = [1/6, 1/6, 1/6, 1/6, 1/6, 1/6;... 7/12, 1/12, 1/12, 1/12, 1/12, 1/12];

To generate a random sequence of states and emissions from the
model, use `hmmgenerate`:

[seq,states] = hmmgenerate(1000,TRANS,EMIS);

The output `seq` is the sequence of emissions
and the output `states` is the sequence of states.

`hmmgenerate` begins in state 1 at step 0,
makes the transition to state *i*_{1} at
step 1, and returns *i*_{1} as
the first entry in `states`. To change the initial
state, see Changing the Initial State Distribution.

Given the transition and emission matrices `TRANS` and `EMIS`,
the function `hmmviterbi` uses
the Viterbi algorithm to compute the most likely sequence of states
the model would go through to generate a given sequence `seq` of
emissions:

likelystates = hmmviterbi(seq, TRANS, EMIS);

`likelystates` is a sequence the same length
as `seq`.

To test the accuracy of `hmmviterbi`, compute
the percentage of the actual sequence `states` that
agrees with the sequence `likelystates`.

sum(states==likelystates)/1000 ans = 0.8200

In this case, the most likely sequence of states agrees with the random sequence 82% of the time.

The functions `hmmestimate` and `hmmtrain` estimate the transition and emission
matrices `TRANS` and `EMIS` given
a sequence `seq` of emissions.

**Using hmmestimate. **The function `hmmestimate` requires
that you know the sequence of states `states` that
the model went through to generate `seq`.

The following takes the emission and state sequences and returns estimates of the transition and emission matrices:

[TRANS_EST, EMIS_EST] = hmmestimate(seq, states) TRANS_EST = 0.8989 0.1011 0.0585 0.9415 EMIS_EST = 0.1721 0.1721 0.1749 0.1612 0.1803 0.1393 0.5836 0.0741 0.0804 0.0789 0.0726 0.1104

You can compare the outputs with the original transition and
emission matrices, `TRANS` and `EMIS`:

TRANS TRANS = 0.9000 0.1000 0.0500 0.9500 EMIS EMIS = 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.5833 0.0833 0.0833 0.0833 0.0833 0.0833

**Using hmmtrain. **If you do not know the sequence of states `states`,
but you have initial guesses for `TRANS` and `EMIS`,
you can still estimate `TRANS` and `EMIS` using `hmmtrain`.

Suppose you have the following initial guesses for `TRANS` and `EMIS`.

TRANS_GUESS = [.85 .15; .1 .9]; EMIS_GUESS = [.17 .16 .17 .16 .17 .17;.6 .08 .08 .08 .08 08];

You estimate `TRANS` and `EMIS` as
follows:

[TRANS_EST2, EMIS_EST2] = hmmtrain(seq, TRANS_GUESS, EMIS_GUESS) TRANS_EST2 = 0.2286 0.7714 0.0032 0.9968 EMIS_EST2 = 0.1436 0.2348 0.1837 0.1963 0.2350 0.0066 0.4355 0.1089 0.1144 0.1082 0.1109 0.1220

`hmmtrain` uses an iterative algorithm that
alters the matrices `TRANS_GUESS` and `EMIS_GUESS` so
that at each step the adjusted matrices are more likely to generate
the observed sequence, `seq`. The algorithm halts
when the matrices in two successive iterations are within a small
tolerance of each other.

If the algorithm fails to reach this tolerance within a maximum
number of iterations, whose default value is `100`,
the algorithm halts. In this case, `hmmtrain` returns
the last values of `TRANS_EST` and `EMIS_EST` and
issues a warning that the tolerance was not reached.

If the algorithm fails to reach the desired tolerance, increase the default value of the maximum number of iterations with the command:

hmmtrain(seq,TRANS_GUESS,EMIS_GUESS,'maxiterations',maxiter)

where `maxiter` is the maximum number of steps
the algorithm executes.

Change the default value of the tolerance with the command:

hmmtrain(seq, TRANS_GUESS, EMIS_GUESS, 'tolerance', tol)

where `tol` is the desired value of the tolerance.
Increasing the value of `tol` makes the algorithm
halt sooner, but the results are less accurate.

Two factors reduce the reliability of the output matrices of `hmmtrain`:

The algorithm converges to a local maximum that does not represent the true transition and emission matrices. If you suspect this, use different initial guesses for the matrices

`TRANS_EST`and`EMIS_EST`.The sequence

`seq`may be too short to properly train the matrices. If you suspect this, use a longer sequence for`seq`.

The posterior state probabilities of an emission sequence `seq` are
the conditional probabilities that the model is in a particular state
when it generates a symbol in `seq`, given that `seq` is
emitted. You compute the posterior state probabilities with `hmmdecode`:

PSTATES = hmmdecode(seq,TRANS,EMIS)

The output `PSTATES` is an *M*-by-*L* matrix,
where *M* is the number of states and *L* is
the length of `seq`. `PSTATES(i,j)` is
the conditional probability that the model is in state `i` when
it generates the `j`th symbol of `seq`,
given that `seq` is emitted.

`hmmdecode` begins with the model in state
1 at step 0, prior to the first emission. `PSTATES(i,1)` is
the probability that the model is in state i at the following step
1. To change the initial state, see Changing the Initial State Distribution.

To return the logarithm of the probability of the sequence `seq`,
use the second output argument of `hmmdecode`:

[PSTATES,logpseq] = hmmdecode(seq,TRANS,EMIS)

The probability of a sequence tends to 0 as the length of the
sequence increases, and the probability of a sufficiently long sequence
becomes less than the smallest positive number your computer can represent. `hmmdecode` returns
the logarithm of the probability to avoid this problem.

By default, Statistics Toolbox hidden Markov model functions
begin in state 1. In other words, the distribution of initial states
has all of its probability mass concentrated at state 1. To assign
a different distribution of probabilities, *p* =
[*p*_{1}, *p*_{2},
..., *p*_{M}],
to the *M* initial states, do the following:

Create an

*M*+1-by-*M*+1 augmented transition matrix, of the following form:where

*T*is the true transition matrix. The first column of contains*M*+1 zeros.*p*must sum to 1.Create an

*M*+1-by-*N*augmented emission matrix, , that has the following form:

If the transition and emission matrices are `TRANS` and `EMIS`,
respectively, you create the augmented matrices with the following
commands:

TRANS_HAT = [0 p; zeros(size(TRANS,1),1) TRANS]; EMIS_HAT = [zeros(1,size(EMIS,2)); EMIS];

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