Sentence examples for prior on each from inspiring English sources

Assuming this prior on each patch (2) refers to the sparse coding of local image patches with bounded prior, hence building a local model from sparse representations.

In their work, 3D shapes are drawn from non-uniform probability distribution functions (PDFs) with a Gaussian prior on each shape in the subspace instead of the common linear subspace model, which is a specific usage of PPCA.

end{aligned} (6 The posterior distribution of xt−1 which is provided by the HB-Kalman reconstruction algorithms is a multivariate Gaussian distribution with mean (x_{t-1|t-1}) and covariance (varSigma _{t-1|t-1}.) As explained in the system equation (Sect. 2.2.1), we place a student's t sparse prior on each element of q t.

Thus, we place a slightly informative prior on each E j + to be small [ E j +~ Beta(1,20)].

This network score is given by the (log) marginal likelihood of the data given the model; equivalent to the Bayes factor when using equal prior on each model structure.

In ridge with mean prior, we set the prior on each network's weight to the average weight that network received in predicting all 1188 GO BP categories with 3 300 annotations.

In (9) we use an inverse-Gamma(1,1) prior on τ, and we assume flat N 0,100) priors on each α coefficient.

Bayesian estimates using these priors are very similar to standard ML estimates of the same logistic regression model.15 For the full logistic MESE model, we used the same priors on the β's in 10, and for τ in 9 we again used an inverse-Gamma(1,1) prior and we assume flat N 0,100) priors on each α coefficient.

For the unadjusted model involving only equation (7), we used flat N 0,10000) priors on each β coefficient, and a Unif 0,1000) prior on σ 2. Bayesian estimates with these priors are extremely similar to OLS estimates.11 For the full MESE model, we used the same priors on the β's and σ 2 in (7), and we fixed the item parameters to their NCES-estimated values in (8).

Priors on each element of B are normal distributions with very large (>10) variances.

Because of the large number of parameters for some subjects, we used a maximum a posteriori (MAP) fitting procedure that placed conjugate priors on each parameter that were fixed across subjects and conditions, subtracting off log(p[ parameter estimate| conjugate prior distribution of parameters]) from the error term in Equation 15.