"most likely trajectory" is a correct and usable phrase in written English.
You can use it when you need to describe a course of action that is thought to be most likely to occur. For example, "We project that the economy's most likely trajectory will continue to be one of slow growth."
Given a Markov chain with n states, the initial state, the final state, and the trajectory length t, we consider the problem of finding the most likely trajectory of length t that the chain traversed.
"When something is as extreme as London, the most expensive place in rich world, the most likely trajectory is change; that it will one day no longer hold the top slot," says Dorling. "But you can't predict what the change will be".
For example, with better information on on-ice feeding rates, our model could be improved to not only estimate boundaries for the energetic impacts of earlier on-shore arrival (Figs 2 and 3) but to also determine the most likely trajectory for future litter size declines.
While it is practically impossible to predict its outcome in a few generations, and beyond the important distinctions between different Arab countries, by far the most likely trajectory of what we have witnessed in the last two years suggests that in the foreseeable future the challenge to those assumptions in the mainstream of the Arab World will only intensify.
Here minimization of the "action" defines the most likely trajectory.
These functions were selected from a larger number of possible functions because they represented the most likely trajectory shapes.
As a result, after perceiving "Jane", the most likely trajectories will lead to regions of state space associated with "Jane" (like "kiss" or whatever other words the ANN due to its prior expectations expects to follow "Jane").
Perceiving a word (like "Jane") will change basins of attraction (depending on prior expectations, i.e. the training set of sentences), and as a consequence the most likely trajectories that subsequently will be followed.
As a reference to compare against, we compute an inherently batch quantity: the most likely state trajectory given all the observations, the so-called the Viterbi path { n 1 : T *, m 1 : T *, r 1 : T * } = arg max n 1 : T, m 1 : T, r 1 : T p ( n 1 : T, m 1 : T, r 1 : T | x 1 : F, 1 : T ).
For the side view, we used the same approach to find the most likely Z trajectory given the already computed X and Y tracks.
The combination of DOT synchronization and population analysis information from one individual enriched by the information coming from the other individuals allowed the estimation of the most likely disease trajectory of the CDR-SOB score.
