Sentence examples for input probability from inspiring English sources

In fact, a misleading computation of the input probability distribution affects groups of bits at once [8].

where p i = Pr{X=x i },i∈{0,..,M-1} is the input probability distribution, and k∈{1,2}.

Using a Direct Simulation Monte Carlo (DSMC) technique on these response surfaces gives smooth probability density functions (PDFs) of the outputs characteristics when input probability characteristics are specified.

This study introduces streamlining Monte Carlo simulation procedures with evaluation of stochastic processes and input probability distribution selection via hypothesis testing, and specification of correlations between simulated variates.

For hard constraint, the input probability is given by a O (t)=(ρ O (t),(θ n (t)|n∈O)) and the overall input probability is (mathbf {a}(t)=left {a_{O_{i}}right }_{i=1,ldots,left (begin {array}{c} N N' end {array} right) }).

Then, for soft constraint, the joint input probability over sub-channel n at time slot t is given by a n (t)=(ρ n (t),θ n (t)) and the overall input probability is denoted by a(t)=(a1 t),...,a N (t)).

In terms of prediction, given a probably inconsistent vector of input probabilities, one has to find the most probable multiple and consistent GO-DAG paths that the protein has to be annotated to.

The task of inferring the most probable latent binary vector given the input probabilities is a decoding problem, which is well-studied in information theory when the underlying structure of constraints has a tree-like structure (including chains).

Uncertainties of input probabilities are propagated through the model using Monte Carlo sampling technique.

This finite state machine smooths the input probabilities given by the MLP using a median filter over a small window.

This paper presents a probabilistic gate model (PGM), which relates the output probability to the error and input probabilities of an unreliable logic gate.