HMM algorithm for hidden Markov models for reasoni
2016-08-23
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Hidden Markov model (HMM) is a statistical model, which is used to describe a Markov process with hidden unknown parameters. The difficulty is to determine the hidden parameters of the process from the observable parameters. These parameters are then used for further analysis, such as pattern recognition. It can be used to solve evaluation problems, decoding problems and learning problems. Given o = o1o2o3 OT and model parameter λ = (a, B, π). For example, there are some HMM with different model parameters, given o = o1o2o3 OT, we want to know which HMM model is most likely to generate the observation sequence. Generally, we use forward algorithm to calculate the probability of each HMM to produce a given observation sequence o, and then select the optimal HMM model. A classic example of this kind of evaluation problem is speech recognition. In the hidden Markov model describing language recognition, each word generates a corresponding HMM, and each observation sequence is composed of the speech of a word. Word recognition is realized by evaluating and selecting the HMM which is most likely to produce the pronunciation represented by the observation sequence. 2. The decoding problem is given as o = o1o2o3 OT and model parameter λ = (a
matlab
算法
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hmm
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诊断
马尔
可夫
推理
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