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These scenarios include: (i) forward probabilities, (ii) dynamic rework probabilities and proportions, (iii) multiple dependency relationships between activities, and (iv) different rework through indirect connections.
The inside and outside probabilities play roles analogous to the backward and forward probabilities in HMM learning respectively.
and p y T |m) is given by Equation 14. Equation 18 is used in Equations 9 and 10 to obtain the forward probabilities.
We conclude this section with some remarks on the calculation of the forward probabilities α T which for a codebook captures how well that codebook matches the context of the T th input segment.
However, with the hard EM only forward probabilities need to be computed to sample from p(Z(i)| h(i), ξ*).
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Considering the message may impair in forwarding, the forward probability is: P={left {P}_E{J}_{fa}right)}^r (9 where P E refers to the surplus energy ratio of the node: r=left|{e}^{mu {d}_{ij}}right| (10).
When a peer forwards a search request, the forward probability is calculated according to its neighbors' degrees and the number of neighbors' objects.
represent the forward probability as in standard HMM theory [36].
The posterior probability P i is the forward probability of five nodes which is 1 (100%).
In this case, when we have only five nodes or less the forward probability is 100%.
The forward probability scheme P i depends on the minimum expected neighbours (d) and the number of neighbours (n).
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