Uncertainty also remains around the probability inputs, given that they were obtained using aggregated data rather than data from a single randomized controlled trial [ 33].
Most of previous studies implement probabilistic-based uncertainty analysis, which requires detailed information to define probability for input factors.
Finally, the Markov derives complement of uptime-steady-state probability as input for FMECA, using limited data; which is an improvement on the current approaches.
To consider the uncertainty in DEA models, different scenarios are formulated with a specified probability for input and output data instead of using point estimates.
In this paper, we propose a bit-width-aware constant-delay run-time Accuracy Programmable Adder (APA) in which the probability of input combinations exhibiting accurate results is programmable and adaptively controlled by the Number of Iterations (NoIs).
To endow the traditional fuzzy rule-based systems (FRBs) with probabilistic features to handle randomness, this paper presents a probabilistic fuzzy neural network (ProFNN) by introducing the probability of input linguistic terms and providing linguistic meaning into the connectionist architecture.
These desynchronization events appeared stochastically with higher probability when input drive was larger.
Here, we take a discrete approach to the problem of TPR violations and we develop an algorithm for the inference of most probable TPR consistent assignments using per-GO term probabilities as input.
However, available crisp probabilities for input events are usually limited, missing or unknown, which introduces data uncertainty.
These ecoregion values served as species establishment probability (SEP) inputs for the forest landscape simulation model, LANDIS-II.
