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A new approach, maximum likelihood approach (MLA), is then developed for determining probabilistic measurements of the fatigue limit data from a small sampling up-and-down test method (SSUDM).
While it is expected that information concerning medical practices is easily evaluated by a panel of experts, probabilistic measurements or abstract information can cause cognitive bias in estimations [ 21, 22].
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That is, the time-varying delay in some NNs may be subject to probabilistic measurement delays.
In [1], the robust (H_{infty}) problem was considered for a discrete-time stochastic GRNs with probabilistic measurement delays.
Here, the probabilistic measurement delays, which narrate the binary shifting sequence, are satisfied by the conditional probability distribution.
The a posteriori probability is calculated from the a priori probability using a probabilistic measurement model h and the current measurement z k.
The most fashionable mechanism to relate the probabilistic measurement delay or some other kind of lacking measurement is to grab it as a Bernoulli distributed white classification [13 20].
Considering the above referenced papers, the robustness of approximation of the stochastic GRNs with leakage delays, distributed delays, and probabilistic measurement delays has not been tackled.
In [2], the robust (H_{infty}) state estimation problem was investigated for a general class of uncertain discrete-time stochastic neural networks with probabilistic measurement delays.
This work predominantly labels the problem of approximation of state variables for discrete-time stochastic genetic regulatory networks with leakage, distributed, and probabilistic measurement delays.
However, the inclusion of discrete-interval GRNs with leakage delays, probabilistic measurement delays, noise, and distributed delays has not been taken into account.
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