Sentence examples for prior such as from inspiring English sources

The variances σ ε 2 and σ A 2 have a conjugate inverse gamma prior, and the Gaussian conjugate prior can be used for the mixing matrix A. For simplicity, the univariate complex normal distribution is introduced as a conjugate prior of source signal S. It is noted that a super-Gaussian prior, such as student-t or Laplace distribution, should be used for speech signals.

However, when using an image based prior, such as the anisotropic diffusion prior, it is convenient to map the solution into a regular grid.

In this case, a prior such as Beta(1,1) may be used for the stratum-specific probability π of the incomplete variable.

The choice of an appropriate tree prior for this dataset is somewhat problematic: on one hand, the bulk of our data is intraspecific, hence a "speciation" prior such as the Yule process may not be adequate.

One may choose the scale-free reference prior (f ϕ = 1/ ϕ) as the least informative option or a so-called "diffuse" prior such as a log-normal with high variance.

These frequencies are nuisance parameters and uncertainty about them will be taken into account by assigning to them non informative prior such as a Beta(1,1) distribution (e.g. [ 78]).

Furthermore, our algorithm is a natural choice for problems with rich topology priors such as object tracking.

Among all the priors, smoothness priors such as the total variation (TV) prior have been widely used in many image processing applications [8].

Several techniques have been used to address this problem by utilizing various priors such as non-negative, support, and Fourier magnitude constraints.

In this paper, we develop noninformative priors such as probability matching priors for the common shape parameter of Weibull models using the asymptotic matching of coverage probabilities of Bayesian credible intervals with the corresponding frequentist coverage probabilities.

'Obvious' priors such as uniform on Ω are actually highly constricting, since they impose a linear relationship on the prior expectations of the π j 's, namely E(π j )=j/k.