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Figure 4 | BMC Bioinformatics

Figure 4

From: Graphical models for inferring single molecule dynamics

Figure 4

(A) The HMM as a GM. At each time step, t, the system occupies a hidden state, z t and produces an observable emission, d t , drawn from p(d t |z t ). In turn, z t is drawn from p(z t |z t −1). (B) Complete GM for the HMM used to describe smFRET data in this work. Following the Bayesian treatment of probability, all unknown parameters are treated as hidden variables, and represented as open circles. Emissions are assumed to be Gaussian, with mean and precision . Transition rates are multinomial, with probabilities given by A. The probability of initially occupying each hidden state is multinomial as well, with probabilities given by . Equations for these distributions are described in the text below Eq. 5. This GM specifies the conditional factorization of shown in Eq. 6.

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