Sentence examples for mean parameter vector from inspiring English sources

with the mean parameter vector given as.

The conditional distribution is, where: for, equal to mean parameter vector of a and.

The second stage model defines the between-individual variability in the parameters as follows θ i = θ + η i where θ is the mean parameter vector for the population and ηi are the individual deviations assumed to be i.i.d. and normal with zero mean vector and covariance matrix Ω.

and, upon reception of a new observation y t+1, proceeds as follows: 1. Estimate the sample mean and covariance of the parameter vector θ t. left{begin{array}{l} mu_{theta_{t}} = sum_{i=1}^{M} theta_{t}^{(m)} w_{t}^{(m)}, Sigma_{theta_{t}} = sum_{i=1}^{M} left(theta_{t}^{(m)} - mu_{theta_{t}}right)left(theta_{t}^{(m)} - mu_{theta_{t}}right)^{top} w_{t}^{(m)} ;. end{array}right.

( g(gamma ) = g(gamma left|varvec{theta}right). ) represents probability density function, ( varvec{theta} ) denotes deep parameter vector, which include mean value, variance or covariance, etc., and ( {varvec{V}}_{ni} (gamma ) ) is measurable utility.

Thus, the parameter vector for the mean ODE approach is θ=(Sw, τon, τoff, δ M g, M0, δ M, α, δ P, σ M g, σ P ).

We set the parameter vector μ x, describing variable-specific means, to the mean of the control group.

- Classification of original data by means of the parameter vectors corresponding to each data.

Again, θ ¯ is a vector of the mean parameters, and σ is the vector of individual deviations from the mean.

Even having a 'right' model and an estimate θ ^ for the parameter vector which fits the data well, does not mean that the whole modeling problem is resolved successfully.

Since the mean channel vector is different to zero for the LOS case, the parameter vector is described in (31) and the computation of the FIM in (34) adds some derivatives that must also be computed.