Call this lottery T. We define a utility function q = u(T) from outcomes to the real (as opposed to ordinal) number line such that if q is the expected prize in T, the agent is indifferent between winning T and winning a lottery T* in which W occurs with probability u(T) and L occurs with probability 1 − u(T).
Figure 10 shows how B, (L_C), and (L_D) varied with the conversion probability, u, i.e. the probability for converting a potential link to a link [16] in the CNN model, and Fig. 11 shows the same in the BA networks in which we varied m, which is the number of links when a new node is added.
However, each cluster also has an associated partition matrix U = { u n ( k, l ) ∈ R | n ∈ ( 1, …, N ), ( k, l ) ∈ Ω ) } which specifies the probability u n (k, l) to which a feature vector θ k, l) belongs to the n th cluster at the TF point (k, l).
Each link l in a temporal reuse set L n ( v k ) is assigned an ordering value Θ n ( v k, l ) ∈ [ 0, | L n ( v k ) | - 1 ], where a smaller value means a higher priority, to establish an ordering policy, so a link l ∈ L n ( v k ) with a higher priority Θ n (v k, l) < Θ n (v k, m) than m ∈ L n ( v k ) will have a higher usage probability U n ( v k, l ) > U n ( v k, m ).
In particular, we assume that during cell division, a resistant mutant is generated with a probability u.
Let us first investigate the level of heterogeneity of a population of type A tumor cells with growth rate R and death rate D, in which neutral alterations arise with probability u per cell division.
Temporal reuse-unaware C -optimised schedules with full rights extension (full temporal reuse) are analysed to estimate the usage probabilities U of links in temporal reuse sets based on their assigned priorities θ.
After many iterations, steady-state P ∞ is denoted as P ∞ = [(1 − η) u ∞ ηv ∞ ] T. In this way, the steady probabilities u ∞ and v ∞ are used to rank the genes and disease phenotypes.
Using these values equation (3) is used to determine the probabilities u i and e ij and these probabilities realised by sampling from a Bernoulli distribution to determine the cycle outcome.
Let us note that in general the above sum includes also the waiting probabilities u l l on a given node l before a random walker performs a jump to a neighboring node l′.
It has one cycle (edges coloured in green); nodes have different exit probabilities u l l ′ (probabilities of passing defined as common fractions; coloured in black, in case of a cycle in green, in case of waiting on a node in cyan), and local time scales τ l l ′ (the cost of passing defined as decimal fractions with arrows; coloured in red).
