Noisy Pattern Search using Hidden Markov Models
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چکیده
describes the starting state probability. The HMM is a generative model which means that we can understand the model by how it generates data. In this case we would sample a hidden state from the distribution p(h1), for example state 3, and then draw a sample from the distribution p(v1|h = 3) by drawing from the distribution represented by B:,3. Then we may draw h2 from A:,3 and continue in this manner until we’ve draw a set of states h1:T and v1:T . Whilst the HMM is best understood in the generative sense we can use it to find out something about the hidden states that gave rise to an observation sequence. That is given the sequence v1:T we wish to infer something about the hidden variables. In particular we often wish to infer:
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تاریخ انتشار 2012