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Segmentation and genome annotation algorithms for identifying chromatin state and other genomic patterns

Fig 2

Two representations of an HMM.

(A) Conditional dependence diagram representation of an unrolled HMM with sequence of hidden states and sequence of observations . In this representation, each node represents a hidden discrete (white rectangle) or observed continuous (gray circle) random variable. For every index t, each hidden random variable Q_t takes on some value q_t; similarly, each observed variable X_t takes on some value x_t. X_t may represent either scalar or vector observations. Solid arcs represent conditional dependence relationships between random variables. (B) State transition diagram representation of Rover and Thomas’s weather example. In this representation, each node represents a potential value of the hidden variable Q_t. The hidden variable takes on values r (rainy) or ¬r (not rainy) on any given day t. Solid arcs represent transitions between hidden states, which have transition probabilities A. HMM, hidden Markov model.

doi: https://doi.org/10.1371/journal.pcbi.1009423.g002