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Prior distribution of CIT and material parameters.
It calculates the prior distribution of the present image using the posterior distribution of the previous adjacent image as well as the prior distribution of image variations.
In the algorithm, Γ distribution is used as the prior distribution of the unknown model parameters.
We set four conditions on prior distribution of factor loadings and thresholds.
In Section 2.5, the estimation of the prior distribution of the adaptation parameter is described.
The prior distribution of the adaptation parameter was estimated from the training models.
We use a Bayesian design algorithm that integrates the D-optimality criterion over a prior distribution of likely parameter values.
The model parameters used are obtained from the prior distribution of initial time t and model parameter m.
Oversampling the less frequent outcomes changes the prior distribution of the response and gives such cases more weight.
The introduction of λ makes the prior distribution of the original signal Laplace, which is already shown in Equation 12.
where p is the prior distribution of the model parameters, p is a priori distribution of the sparse coefficient, p(h| Ω) is the prior distribution of the point spread function, and p(P| α, h, Ω) is the prior distribution of the noisy projection.
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