We get a probability of 46 that the best scoring

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Unformatted text preview: πi = k in the state path π), normalized by the sum over all possible state paths. In our toy model, this is just one state path in the numerator and a sum over 14 state paths in the denominator. We get a probability of 46% that the best-scoring fifth G is correct and 28% that the sixth G position is correct (Fig. 1, bottom). This is called posterior decoding. For larger problems, posterior decoding uses two dynamic programming algorithms called Forward and Backward, which are essentially like Viterbi, but they sum over possible paths instead of choosing the best. Making more realistic models Making an HMM means specifying four things: (i) the symbol alphabet, K different symbols (e.g., ACGT, K = 4); (ii) the number of states in the model, M; (iii) emission probabilities ei(x) for each state i, that sum to one over K symbols x, Σ ei(x) = 1; and x (iv) transition probabilities ti( j ) for each state i going to any other state j (including itself) that sum to one over the M states j, Σ ti( j ) = 1. For example, in our toy splice-site model, maybe we’re not happy with our d...
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This note was uploaded on 04/06/2010 for the course COMPUTER S COSC1520 taught by Professor Paul during the Spring '09 term at York University.

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