Forward–backward algorithm: Difference between revisions

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{{Short description|Inference algorithm for hidden Markov models}}

{{Inline|date=April 2018}}

The '''forward–backward algorithm''' is an [[Statistical_inference | inference]] [[algorithm]] for [[hidden Markov model]]s which computes the [[posterior probability|posterior]] [[marginal probability|marginals]] of all hidden state variables given a sequence of observations/emissions <math>o_{1:T}:= o_1,\dots,o_T</math>, i.e. it computes, for all hidden state variables <math>X_t \in \{X_1, \dots, X_T\}</math>, the distribution <math>P(X_t\ |\ o_{1:T})</math>. This inference task is usually called '''smoothing'''. The algorithm makes use of the principle of [[dynamic programming]] to efficiently compute the values that are required to obtain the posterior marginal distributions in two passes. The first pass goes forward in time while the second goes backward in time; hence the name ''forward–backward algorithm''.

The term ''forward–backward algorithm'' is also used to refer to any algorithm belonging to the general class of algorithms that operate on sequence models in a forward–backward manner. In this sense, the descriptions in the remainder of this article refer butonly to one specific instance of this class.

==Overview ==

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==Forward probabilities==

The following description will use matrices of probability values ratherinstead thanof probability distributions. However, althoughit inis generalimportant to note that the forward-backward algorithm can generally be applied to both continuous as well asand discrete probability models.

We transform the probability distributions related to a given [[hidden Markov model]] into matrix notation as follows.

The transition probabilities <math>\mathbf{P}(X_t\mid X_{t-1})</math> of a given random variable <math>X_t</math> representing all possible states in the hidden Markov model will be represented by the matrix <math>\mathbf{T}</math> where the column index <math>ij</math> will represent the target state and the row index <math>ji</math> represents the start state. A transition from row-vector state <math>\mathbf{\pi_t}</math> to the incremental row-vector state <math>\mathbf{\pi_{t+1}}</math> is written as <math>\mathbf{\pi_{t+1}} = \mathbf{\pi_{t}} \mathbf{T}</math>. The example below represents a system where the probability of staying in the same state after each step is 70% and the probability of transitioning to the other state is 30%. The transition matrix is then:

:<math>\mathbf{T} = \begin{pmatrix}

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</math>

In a typical Markov model, we would multiply a state vector by this matrix to obtain the probabilities for the subsequent state. In a hidden Markov model the state is unknown, and we instead observe events associated with the possible states. An event matrix of the form:

:<math>\mathbf{B} = \begin{pmatrix}

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:<math>\mathbf{P}(O = j)=\sum_{i} \pi_i B_{i,j}</math>

ThisThe probability of a given state leading to the observed event j can be represented in matrix form by multiplying the state row-vector (<math>\mathbf{\pi}</math>) bywith an observation matrix (<math>\mathbf{O_j} = \mathrm{diag}(B_{*,o_j})</math>) containing only diagonal entries. Each entry is the probability of the observed event given each state. Continuing the above example, anthe observation ofmatrix for event 1 would be:

:<math>\mathbf{O_1} = \begin{pmatrix}

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</math>

We can now make this general procedure specific to our series of observations. Assuming an initial state vector <math>\mathbf{\pi}_0</math>, (which can be optimized as a parameter through repetitions of the forward-backbackward procedure), we begin with <math>\mathbf{f_{0:0}} = \mathbf{\pi}_0</math>, then updating the state distribution and weighting by the likelihood of the first observation:

:<math>

\mathbf{f_{0:1}} = \mathbf{\pi}_0 \mathbf{T} \mathbf{O_{o(1)o_1}}

</math>

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:<math>

\mathbf{f_{0:t}} = \mathbf{f_{0:t-1}} \mathbf{T} \mathbf{O_{o(t)o_t}}

</math>

This value is the forward unnormalized [[probability vector]]. The i'th entry of this vector provides:

:<math>

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:<math>

\mathbf{\hat{f}_{0:t}} = c_t^{-1}\ \mathbf{\hat{f}_{0:t-1}} \mathbf{T} \mathbf{O_{o(t)o_t}}

</math>

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</math>

Notice that we are now using a [[Row and column vectors|column vector]] while the forward probabilities used row vectors. We can then work backwards using:

:<math>

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To understand this, we note that <math>\mathbf{f_{0:t}}(i) \cdot \mathbf{b_{t:T}}(i)</math> provides the probability for observing the given events in a way that passes through state <math>x_i</math> at time t. This probability includes the forward probabilities covering all events up to time t as well as the backward probabilities which include all future events. This is the numerator we are looking for in our equation, and we divide by the total probability of the observation sequence to normalize this value and extract only the probability that <math>X_t=x_i</math>. These values are sometimes called the "smoothed values" as they combine the forward and backward probabilities to compute a final probability.

The values <math>\mathbf{\gamma_t}(i)</math> thus provide the probability of being in each state at time t. As such, they are useful for determining the most probable state at any time. It should be noted, however, that theThe term "most probable state" is somewhat ambiguous. While the most probable state is the most likely to be correct at a given point, the sequence of individually probable states is not likely to be the most probable sequence. This is because the probabilities for each point are calculated independently of each other. They do not take into account the transition probabilities between states, and it is thus possible to get states at two moments (t and t+1) that are both most probable at those time points but which have very little probability of occurring together, i.e. <math> \mathbf{P}(X_t=x_i,X_{t+1}=x_j) \neq \mathbf{P}(X_t=x_i) \mathbf{P}(X_{t+1}=x_j) </math>. The most probable sequence of states that produced an observation sequence can be found using the [[Viterbi algorithm]].

==Example ==

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</math>

Notice that the [[transformation matrix]] is also transposed, but in our example the transpose is equal to the original matrix. Performing these calculations and normalizing the results provides:

:<math>

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</math>

For the backward probabilities, we start with:

:<math>

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</math>

Finally, we will compute the smoothed probability values. These result alsoresults must also be scaled so that its entries sum to 1 because we did not scale the backward probabilities with the <math>c_t</math>'s found earlier. The backward probability vectors above thus actually represent the likelihood of each state at time t given the future observations. Because these vectors are proportional to the actual backward probabilities, the result has to be scaled an additional time.

:<math>

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==Performance ==

The brute-forceforward–backward procedurealgorithm forruns thewith solutiontime ofcomplexity this<math> problemO(S^2 isT) the</math> generationin ofspace all<math> possibleO(S T) </math>, where <math>N^T</math> stateis sequencesthe andlength calculatingof the jointtime probabilitysequence ofand each<math>S</math> stateis sequencethe withnumber of symbols in the observedstate seriesalphabet.<ref>[[#RussellNorvig10|Russell of& eventsNorvig 2010 pp. 579]]</ref> This approachThe hasalgorithm can also run in constant space with [[time complexity]] <math> O(T \cdot NS^2 T^2) </math>, whereby recomputing values at each step.<mathref>T[[#RussellNorvig10|Russell & Norvig 2010 pp. 575]]</mathref> isFor thecomparison, lengtha of[[Brute-force sequencessearch|brute-force andprocedure]] would generate all possible <math>NS^T</math> isstate sequences and calculate the numberjoint probability of symbolseach instate sequence with the stateobserved alphabetseries of events, which would have [[time complexity]] <math> O(T \cdot S^T) </math>. ThisBrute force is intractable for realistic problems, as the number of possible hidden node sequences typically is extremely high. However, the forward–backward algorithm has time complexity <math> O(N^2 T)\, </math>.

An enhancement to the general forward-backward algorithm, called the [[Island algorithm]], trades smaller memory usage for longer running time, taking <math> O(NS^2 T \log T)\, </math> time and <math> O(NS \log T)\, </math> memory. OnFurthermore, ait computeris withpossible anto unlimitedinvert numberthe ofprocess processors,model thisto canobtain bean reduced<math>O(S)</math> tospace, <math> O(NS^2 T)\, </math> totaltime timealgorithm, whilealthough stillthe takinginverted onlyprocess <math>may O(Nnot \logexist T)\,or </math>be memory[[ill-conditioned]].<ref>J.{{cite journal |last1=Binder, K.|first1=John |last2=Murphy and S.|first2=Kevin |last3=Russell. |first3=Stuart |title=Space-Efficientefficient Inferenceinference in Dynamicdynamic Probabilistic Networks.probabilistic networks |journal=Int'l, Joint Conf. onOn Artificial Intelligence, |date=1997 |url=https://www.cs.ubc.ca/~murphyk/Papers/ijcai97.pdf |access-date=8 July 2020}}</ref>

In addition, algorithms have been developed to compute <math>\mathbf{f_{0:t+1}}</math> efficiently through online smoothing such as the fixed-lag smoothing (FLS) algorithm .<ref>[[#RussellNorvig10|Russell & Norvig 2010 Figure 15.6 pp. 580]].</ref>

==Pseudocode==

<pre>

Backward(guessState, sequenceIndex):

if sequenceIndex is past the end of the sequence, return 1

if (guessState, sequenceIndex) has been seen before, return saved result

result = 0

for each neighboring state n:

result = result + (transition probability from guessState to

n given observation element at sequenceIndex)

* Backward(n, sequenceIndex+1)

save result for (guessState, sequenceIndex)

return result

</pre>

'''algorithm''' forward_backward '''is'''

==Python example==

'''input:''' guessState

Given HMM (just like in [[Viterbi algorithm]]) represented in the [[Python programming language]]:

int ''sequenceIndex''

<source lang="python">

'''output:''' ''result''

states = ('Healthy', 'Fever')

end_state = 'E'

'''if''' ''sequenceIndex'' is past the end of the sequence, return 1'''then'''

observations = ('normal', 'cold', 'dizzy')

'''return''' 1

'''if''' (''guessState'', ''sequenceIndex'') has been seen before, return saved result'''then'''

'''return''' saved result

''result'' := 0

start_probability = {'Healthy': 0.6, 'Fever': 0.4}

'''for each''' neighboring state n:

transition_probability = {

''result'' := result + (transition probability from ''guessState'' to

'Healthy' : {'Healthy': 0.69, 'Fever': 0.3, 'E': 0.01},

n given observation element at ''sequenceIndex'')

'Fever' : {'Healthy': 0.4, 'Fever': 0.59, 'E': 0.01},

* × Backward(n, ''sequenceIndex'' + 1)

}

Backward save result for (''guessState'', ''sequenceIndex''):

'''return''' ''result''

==Python example==

Given HMM (just like in [[Viterbi algorithm]]) represented in the [[Python programming language]]:

<sourcesyntaxhighlight lang="python">

states = ('"Healthy'", '"Fever'")

end_state = '"E'"

observations = ('"normal'", '"cold'", '"dizzy'")

start_probability = {'"Healthy'": 0.6, '"Fever'": 0.4}

transition_probability = {

'Healthy' "Healthy": {'"Healthy'": 0.69, '"Fever'": 0.3, '"E'": 0.01},

'Fever' "Fever": {'"Healthy'": 0.4, '"Fever'": 0.59, '"E'": 0.01},

}

emission_probability = {

'Healthy' "Healthy": {'"normal'": 0.5, '"cold'": 0.4, '"dizzy'": 0.1},

'Fever' "Fever": {'"normal'": 0.1, '"cold'": 0.3, '"dizzy'": 0.6},

}

</syntaxhighlight>

</source>

We can write the implementation of the forward-backward algorithm like this:

<sourcesyntaxhighlight lang="python">

def fwd_bkw(observations, states, start_prob, trans_prob, emm_prob, end_st):

# forward part of the"""Forward–backward algorithm."""

# Forward part of the algorithm

fwd = []

f_prev = {}

for i, observation_i in enumerate(observations):

f_curr = {}

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prev_f_sum = start_prob[st]

else:

prev_f_sum = sum(f_prev[k] * trans_prob[k][st] for k in states)

f_curr[st] = emm_prob[st][observation_i] * prev_f_sum

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p_fwd = sum(f_curr[k] * trans_prob[k][end_st] for k in states)

# backwardBackward part of the algorithm

bkw = []

for i, observation_i_plus in enumerate(reversed(observations[1:] + (None,))):

b_prev = {}

for i, observation_i_plus in enumerate(reversed(observations[1:]+(None,))):

b_curr = {}

for st in states:

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p_bkw = sum(start_prob[l] * emm_prob[l][observations[0]] * b_curr[l] for l in states)

# mergingMerging the two parts

posterior = []

for i in range(len(observations)):

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assert p_fwd == p_bkw

return fwd, bkw, posterior

</syntaxhighlight>

</source>

The function <code>fwd_bkw</code> takes the following arguments:

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In the running example, the forward-backward algorithm is used as follows:

<sourcesyntaxhighlight lang="python">

def example():

return fwd_bkw(
observations,

states,

start_probability,

transition_probability,

emission_probability,

end_state),

} )

</source>

</syntaxhighlight>

<source lang="python">

<syntaxhighlight lang="pycon">

>>> for line in example():

... print(*line)

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{'Healthy': 0.0010418399999999998, 'Fever': 0.00109578} {'Healthy': 0.00249, 'Fever': 0.00394} {'Healthy': 0.01, 'Fever': 0.01}

{'Healthy': 0.8770110375573259, 'Fever': 0.1229889624426741} {'Healthy': 0.623228030950954, 'Fever': 0.3767719690490461} {'Healthy': 0.2109527048413057, 'Fever': 0.7890472951586943}

</syntaxhighlight>

</source>

==See also ==

* [[Baum-WelchBaum–Welch algorithm]]

* [[Viterbi algorithm]]

* [[BCJR algorithm]]

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== References==

{{reflist}}

* [[Lawrence Rabiner|Lawrence R. Rabiner]], A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. ''Proceedings of the [[IEEE]]'', 77 (2), p.&nbsp;257–286, February 1989. [https://dx.doi.org/10.1109/5.18626 10.1109/5.18626]

* {{cite journal |author=Lawrence R. Rabiner, B. H. Juang|title=An introduction to hidden Markov models|journal=IEEE ASSP Magazine |date=January 1986 |pages=4–15}}

* {{cite book | author = Eugene Charniak|title = Statistical Language Learning|publisher = MIT Press| ___location=Cambridge, Massachusetts|year = 1993|isbn=978-0-262-53141-2}}

* <cite id = RussellNorvig10>{{cite book | author = Stuart Russell and Peter Norvig|title = Artificial Intelligence A Modern Approach 3rd Edition|publisher = Pearson Education/Prentice-Hall|___location = Upper Saddle River, New Jersey|year = 2010|isbn=978-0-13-604259-4}}</cite>

<cite id = RussellNorvig10>

*{{cite book | author = Stuart Russell and Peter Norvig|title = Artificial Intelligence A Modern Approach 3rd Edition|publisher = Pearson Education/Prentice-Hall|___location = Upper Saddle River, New Jersey|year = 2010|isbn=978-0-13-604259-4}}

==External links ==

* [http://www.cs.jhu.edu/~jason/papers/#eisner-2002-tnlp An interactive spreadsheet for teaching the forward–backward algorithm] (spreadsheet and article with step-by-step walk-through)

* [httphttps://www.cs.brown.edu/research/ai/dynamics/tutorial/Documents/HiddenMarkovModels.html Tutorial of hidden Markov models including the forward–backward algorithm]

* [http://code.google.com/p/aima-java/ Collection of AI algorithms implemented in Java] (including HMM and the forward–backward algorithm)

{{DEFAULTSORT:Forward-backward algorithm}}

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