Characterizing the downstream consequences of defects involving attachment organelle components has made it possible to begin to elucidate the probable sequence of certain events in the biogenesis of this structure.
Fortunately, initiatives like the Dominantly Inherited Alzheimer Network and the Colombian Alzheimer's Prevention Initiative Registry are currently collecting such data in large cohorts of presymptomatic individuals and cross-sectional analyses have already provided insights into the probable sequence of biomarker changes (Bateman et al., 2012; Reiman et al., 2012).
Modeling potential domino scenarios in process plants includes the prediction of the most probable sequence of events and the calculation of respective probabilities, so-called escalation probabilities, so that appropriate prevention and mitigation safety measures can be devised.
The Viterbi algorithm is then used to find the most probable sequence [18], with the Euclidian-squared distance given by (10).
are then object of Viterbi decoding used to find the most probable sequence, similarly to the previous scheme, with the corresponding trellis code weights for time slot given by (14), and with represented in (9): (14).
A dynamic programming algorithm, the Viterbi algorithm was applied to predict the Viterbi path which generates the most probable sequence of hidden states representing discrete copy numbers along the chromosomes [ 64].
Since we wanted to find the most probable sequence of transitions among the attractors representing the various cell types, we followed the changes in the probability of reaching a certain attractor throughout time given that the system was initialized in a particular attractor at time t = 0 (see Figure 3).
evaluation: determine the probability of occurrence of the observed sequence; learning: determine the most likely emissions and transition; decoding (Viterbi): determine the most probable sequence of states emitting the observed sequence.
Given the above, there are three classes of problems in which the HMM can be used to solve [34, 35]: (1) evaluation: determine the probability of occurrence of the observed sequence; (2) learning: determine the most likely emissions and transition; (3) decoding (Viterbi): determine the most probable sequence of states emitting the observed sequence. .
The MLSE algorithm is able to optimally estimate the most probable sequence of transmitted symbols/codewords (depending on equalization/decoding), while the MAP algorithm exactly estimates the probability of each transmitted symbol/codeword.
In general, if we can determine the probabilities for sequences of states, we can also determine the most probable sequence of words or phonemes; that is, given a sequence of observations, we calculate the state distribution and subsequently a distribution over phonologies, to wit the probabilities of possible word, syllable, or phoneme sequences.
