Image that we close our eyes , and choose a coin between the two
biased coin that we have prepared . Assume that the first coin have
80 % for us to get head and 20% for us to get tail , and the second
coin have 70 % for us to get head and 30% for us to get tail . While
we choose the first(second) coin , next time we have 30% (50%)
to choose the same coin again , and 70%(50%) to choose another coin .
Then we can use Hidden Makov Model to describe phenomenon
of choosing a coin and toss it to see what we get (head or tai) .
If we observe ” HHHTTTTHHTTTTTTTHHHH ” sequence of doing
above activity . Then we can use this model to predict the Coin ( first or second)
– Toss pair for the sequence . Since we close our eyes , we can not see which
coin we choose to make a toss ( States are hidden for us , so it was called
Hidden Markov Model) .
Example 2 : In activity recognition , activities can be modeled the states , and
the things we touch while we are doing some specific activity can be modeled as
the observations . Then while we collect enough data , we can use them to
train the HMM and learn the parameters (transition probabilities, etc) of HMM