We designate p i (t) ((i=1,2,ldots,K )), as the probability of process G t) staying in state i at instant ( t ge 0 ).
And Schaffer (2001) suggests a generalization of this approach, on which causal connection is understood in terms of the probabilities of processes.
Previous works assume that the number of faulty processes or the probability of a process acting maliciously is known to the master, e.g., as in model Fb.
The diagrams are part of Feynman's very efficient and elegant algorithm for computing the probability of scattering processes.
Schaffer (2001) offers an account according to which causes raise the probability of specific processes, rather than individual events.
Greater rotational and vibrational freedom leads to a greater probability of nonradiative processes and hence a lower fluorescence quantum yield.
So, for a given process, if two probability amplitudes, 'v' and 'w', are involved, the probability of the process will be given either by P=|\mathbf{v}+\mathbf{w}|^2 or P=|\mathbf{v}\times\mathbf{w}|^2.
Denote by p ( ξ, t, d ζ ) the transition probability of the process Y ( t ) = X t , then Y ( t ) is a time homogeneous Markov process according to Mohammed [15].
And it might also be the case that Bob's aiming raised the probability of the process producing the soaking — Bob's aiming might have raised the chance of the spillage process, by threatening the aquarium.
However, in quantum mechanics, possibilities have to be added and then squared, to get the probability of a process actually occurring.
