Sentence examples for matrix of probability from inspiring English sources

The matrix of probability transition function, the probability of defect detection and the probability distribution of sizing a detected defect is incorporated in estimating the probability of failure.

These soft belief functions are formulated on the basis of a confusion matrix of probability mass functions obtained jointly from both acoustic and visual speech features.

Transitions are governed by a matrix of probability values.

Suppose S and S′ are the node voltage matrix of probability graph g and g′, k is graph node size of g and g′, so there is k kind of possible node mapping relations of S i (i = 1,..., k) and S j (j = 1,..., k), S i and S j is the node voltage sequence in corresponding g and g′.

We introduce a contact matrix of probability distributions that takes into account the heterogeneity of contact durations between (and within) classes of individuals, and we show that, in the case study presented, this representation yields a good approximation of the epidemic spreading properties obtained by using the high-resolution data.

Using a predefined threshold, we can estimate the number of sources, the DOAs of the sources, and a matrix of probabilities which we call "Probability Mask" in Fig. 1.

This involves quantifying the full matrix of probabilities for the particular unrest signs being present or absent, conditional on each of the volcanic states being true (n.b. inference of the latter states is unavoidable because they cannot be observed or determined independently).

The result of the first simulation was exact, according to the initial cohort characteristics and the transition matrix of probabilities.

The transition matrix T is a square matrix of probabilities changing between the tRNAstates, where the sum of all transitions leaving or staying in a state is one (∑ jt ij = 1).

The procedure is based on representing the network as a matrix of probabilities that reflect random walks on a simple graph, similar to that used in the well-known Markov Clustering (MCL) algorithm [ 9].

If P1 is the ng × nf matrix of probabilities corresponding to the first QTL chosen, for a multitrait analysis the model (2) becomes u g = (I t ⊗ P 1 ) a 1 t + u p and (4) becomes u g = (I t ⊗ P 1 ) a 1 t + (I t ⊗ P − 1 ) a − 1 + u p, where a1 t is the vector of trait-by-founder effect sizes for the first QTL.