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The content presented here is a collection of my notes and personal insights from two seminal papers on hmms by rabiner in 1989 [2] and ghahramani in 2001 [1], and also from kevin murphy’s book [3]. The markov process|which is hidden behind the dashed line|is determined by the current state and the a matrix. Here we have to determine the best sequence of hidden states, the one that most likely produced word image
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This is an application of decoding problem. A generic hidden markov model is illustrated in figure 1, where the xi represent the hidden state sequence and all other notation is as given above Given a cover image and a binary message, the hidden encoder produces a visually indistinguishable encoded image that contains the message, which can be recovered with high accuracy by the decoder.
We next introduce hidden markov models and show how the the additional features of hmms allow for the inference of precise boundaries
In subsequent lectures, we will explore how hmms can be used to model indels and, up to a point, positional dependence. Hidden markov models formalize sequential observation of a system without perfect access to state (i.e., state is \hidden) a variety of inference problems can be solved using straightforward dynamic programming algorithms the learning (parameter tting) problem is best done with \supervised data { i.e., state & observation trajectories.
