Nowadays, many applications use Hidden Markov Models (HMMs) to solve crucial issues such as bioinformatics, speech recognition, musical analysis, digital signal processing, data mining, financial applications, time series analysis and many others. HMMs are probabilistic models which are very useful to model sequence behaviours or discrete time series events. Formally it models Markov processes with hidden states, like an extension for Markov Chains. For computer scientists, is a state machine with probabilistic transitions where each state can emit a value with a given probability.
For better understanding HMMs, I will illustrate how it works with "The Fair Bet Casino" problem. Imagine you are in a casino where you can bet on coins tosses, tossed by a dealer. A coin toss can have two outcomes: head (H) or tail (T). Now suppose that the coin dealer has two coins, a fair (F) which outputs both H and T with 1/2 probabilities and a biased coin (B) which outputs H with probability 3/4 and T with 1/4. Using probability language we say:
- P(Hi+1|Fi) = 1/2
- P(Ti+1|Fi) = 1/2
- P(Hi+1|Bi) = 3/4
- P(Ti+1|Bi) = 1/4
Now imagine that the dealer changes the coin in a way you can't see, but you know that he does it with a 1/10 probability. So thinking the coin tosses as a sequence of events we can say:
- P(Fi+1|Fi) = 9/10
- P(Bi+1|Fi) = 1/10
- P(Bi+1|Bi) = 9/10
- P(Fi+1|Bi) = 1/10
We can model it using a graph to illustrate the process:
 |
| HMM for "The Fair Bet Casino" problem |
That's a HMM! It isn't any rocket science. Is just important to add a few remarks. We call the set of all possible emissions of the Markov process as the alphabet Σ ({H, T} in our problem). For many of computational method involving HMMs you will also need a initial state distribution π. For our problem we may assume that the we have equal probability for each coin.
Now comes in our mind what we can do with the model in our hands. There are lot's of stuff to do with it, such as: given a sequence of results, when the dealer used the biased coin or even generate a random sequence with a coherent behaviour when compared to the model.
There is a nice library called ghmm (available for C and Python) which handles HMMs and already gives us the most famous and important HMM algorithms. Unfortunately the python wrapper is not pythonic. Let's model our problem in python to have some fun:
Now comes in our mind what we can do with the model in our hands. There are lot's of stuff to do with it, such as: given a sequence of results, when the dealer used the biased coin or even generate a random sequence with a coherent behaviour when compared to the model.
There is a nice library called ghmm (available for C and Python) which handles HMMs and already gives us the most famous and important HMM algorithms. Unfortunately the python wrapper is not pythonic. Let's model our problem in python to have some fun:
import ghmm
# setting 0 for Heads and 1 for Tails as our Alphabet
sigma = ghmm.IntegerRange(0, 2)
# transition matrix: rows and columns means origin and destiny states
transitions_probabilities = [
[0.9, 0.1], # 0: fair state
[0.1, 0.9], # 1: biased state
]
# emission matrix: rows and columns means states and symbols respectively
emissions_probabilities = [
[0.5, 0.5], # 0: fair state emissions probabilities
[0.75, 0.25], # 1: biased state emissions probabilities
]
# probability of initial states
pi = [0.5, 0.5] # equal probabilities for 0 and 1
hmm = ghmm.HMMFromMatrices( sigma, # you can model HMMs with others emission probability distributions
ghmm.DiscreteDistribution(sigma), transitions_probabilities,
emissions_probabilities,
pi
)>>> print hmmDiscreteEmissionHMM(N=2, M=2)
state 0 (initial=0.50)
Emissions: 0.50, 0.50
Transitions: ->0 (0.90), ->1 (0.10)
state 1 (initial=0.50)
Emissions: 0.75, 0.25
Transitions: ->0 (0.10), ->1 (0.90)Now that we have our HMM object on the hand we can play with it. Suppose you have the given sequence of coin tosses and you would like to distinguish which coin was being used at a given state:
tosses = [1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1]The viterbi algorithm can be used to trace the most probable states at each coin toss according to the HMM distribution:
# not as pythonic is could be :-/
sequence = ghmm.EmissionSequence(sigma, tosses)
viterbi_path, _ = hmm.viterbi(sequence)
>>> print viterbi_path
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]Nice! But sometimes is interesting to have the probability of each state on the point instead of only the most probable one. To have that, you must use the posterior or forward algorithms to have more detailed information.
states_probabilities = hmm.posterior(sequence)
>>> print states_probabilities [[0.8407944139086141, 0.1592055860913865], [0.860787703168127, 0.13921229683187356], ... ]The posterior method result, returns the list of probabilities at each state, for example, in the first index we have
[0.8407944139086141, 0.1592055860913865]. That means that we have ~0.84 probability of chance that the dealer is using the fair coin and ~0.16 for the biased coin. We also can plot a graph to show the behaviour of the curve of the probability of the dealer being using the fair coin (I used matplotlib for the graphs). |
| Probability of being a fair coin over time |
This is only a superficial example of what can HMMs do. It's worthy give a look at it if you want do some sequence or time series analysis in any domain. I hope this post presented and cleared what are HMM and how they can be used to analyse data.
