Sentence examples for probability component from inspiring English sources

The probability component of the risk concept should be replaced by uncertainty.

This is done through the introduction of three parameters for each failure cause, which relate to physical attributes of the system being modeled, i.e., cause condition probability, component fragility, and coupling factor strength.

In particular, when the mixing probability component of the model is fixed either with or without covariates, the MZIP and MZINB models may be viewed as special cases of corresponding MPois-Pois and MNB-Pois models where the Poisson component of the latter two models has a mean of zero, rendering that component degenerate.

To do this, we assume that packets are correctly received by a probability expressed as p L =p0p L, where L is the packet length in bits, 1−p can be considered the bit error rate (BER), and p0 is a non-size-dependent delivery probability component, such as the receiver failing to synchronize to the preamble and collisions.

Pan argued that the probability component of model-based methods fits very well the highly variable nature of biological data and gives a broad range of possibilities to include biological prior knowledge.

The ad hoc high probability components (Q k > 0.9) corresponding to the sites with a high probability of contribution to functional divergence were variable between different pairwise comparisons.

Although prior studies identify the causes associated with the gap in life expectancy, they do not indicate which mechanism – the difference-in-age component (or simply age component) or the difference-in-probability component (or incidence component) – is driving the difference.

Factorial HMMs (Ghahramani and Jordan, 1997) are HMMs in which the hidden variable is a vector S i = (S i,…, S i k ) with values drawn from some Cartesian product H1 × … × H k and with a transition probability defined component by component for i = 2,…, L via (2) and for the first slot, P S1 = (s1,…, s1 k )) = ∏ j =1 k P j (s1 j ).

Factored HMMs (Ghahramani and Jordan, 1997) are HMMs in which the hidden variable is a vector S i = (S i,…, S i k ) with values drawn from a Cartesian product H1 × ··· × H k and with a transition probability defined component by component for i = 2,…, L via (3) and for the first slot, P S1 = (s1,…, s1 k )) = ∏ j =1 k P j (s1 j ).

However, greater number of redundant components increases the probability of component failure.

Von Neumann recognized that there was a discrepancy between the theory of automata and the practice of building and operating computing machines because the theory did not take into account the realistic probability of component failure.