Consider this scenario: a guy (let’s call him John) has three dices, Dice 1, 2 and 3. The shapes of the dices are different. Dice 1 has number [1, 2, 3, 4, 5, 6] on it, Dice 2 has number [1, 2, 3, 4] and Dice 3 has [1, 2, 3, 4, 5, 6, 7, 8], as seen in the following figure.
John throws one dice each time and the probability he picks the next dice is based on his previous selection. For example, he is more LIKELY to pickup Dice 1 if he just picked Dice 2 last time, and he UNLIKELY to pick Dice 1 if he just picked Dice 3 last time. We do not know which dice he selected but we can see the number shown on the dice. Now, after the observation of a sequence of throwing dices, what do you think the next number would be?
This may sound a very difficult question but actually in linguistics the researchers are dealing with this kind of problem all the time. It’s like you can HEAR the sound of each word every time and based on the HIDDEN connection rule of the words (i.e. syntax and meaning) we want to predict what the next word could be. Mathematical models were built to represent this type of question. In this example, the states are determined by its previous state(s) and we call it Markov Model, or Markov Chain. A simple case is the state is determined by its previous one state — a Markov chain of order 1. Also, which dice (state) was selected is not know and instead, the consequence of the state (number) can be observed. It is called Hidden Markov Model.
There are three problems in HMM that need be addressed. They are 1) Evaluation: Given the probability of the state transmission and the probability of the shown observations of each hidden state (I.e. for a given HMM), calculate the probability of an observed sequence. 2) Decoding: Given the HMM and the observed sequence, what is the most likely hidden states happened behind this. 3) Learning: Given the observed sequence, estimate the HMM. As we can see from this, the third problem is the most difficult one.
Hidden Markov Model (HMM) is a powerful tool for analyzing the time series signal. There is a good tutorial explaining the concept and the implementation of HMM. There are codes implementing HMM in different languages such as C, C++, C#, Python, MATLAB and Java etc. Unfortunately I failed to find one implemented in LabVIEW. This may be a reinvention of the wheel, but instead of calling the DLLs in LabVIEW, I built one purely in LabVIEW with no additional add-ons needed.
Multiple references were used to implement this LabVIEW HMM toolkit. , , , . The test demo of forward algorithm, backward algorithm and Viterbi Algorithm in the code referenced .
The following demo analyzed the hidden states of a chapter of texts. You can find the detailed description in . The following figure is the observed sequence of the HMM model. There are about 50,000 characters (including space) in this text. All punctuations were removed and only the space and letters were kept as the hidden states. Thus there are 27 states, State 0 to 26, of which State 0 = Space, State 1 = a/A, State 2 = b/B and so on.
With no prior knowledge of this text, or even English, we initialize a HHM model that has two hidden states. The probability of propagating from one state to another is unknown yet. The 26 letters are the observed phenomenons of the hidden states. The probability of each letter in State 1 is plotted in dots, and the probability of each letter is plotted in line in the 2nd state.
Running the forward-backward algorithm in HMM we obtained two states: Letters A, E, I, O, U more likely to appear in State 1 while the rest letters more likely to appear in State 2. So with no specified rules or prior knowledge we managed to divide the letters into vowels and consonants. 🙂
 http://www.52nlp.cn/hmm-learn-best-practices-one-introduction (Chinese)