# 9.5: Hidden Markov Chains

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A toy Hidden Markov Model is a generative approach to model this behavior. Each emission of the HMM is one DNA base/letter. The hidden states of the model are intergenic, exon, intron. Improving upon this model would involve including hidden states DonorG and DonorT. The DonorG and DonorT states utilize the information that exons are delineated by GT at the end of the sequence before the start of an intron. (See Figure 9.4 for inclusion of DonorG and DonorT into the model)

The e in each state represents emission probabilities and the arrows indicate the transition probabilities.

Aside from the initial assumptions, additional evidence such as evolutionary conservation and experi- mental mRNA data can help create an HMM to better model the behavior. (See Figure 9.5)