The phrase "approximation of the prior" is correct and usable in written English.
It can be used in contexts related to statistics, probability, or Bayesian analysis where one is discussing estimates or predictions based on previous information.
Example: "The model's performance improved significantly after incorporating an approximation of the prior distribution."
Alternatives: "estimate of the prior" or "approximate prior".
The affine transformation is chosen to be such that after transformation the split dimension is aligned with the direction of the maximum nonlinearity and such that the resulting mixture is a good approximation of the prior component.
BinoGMF uses BinoGM in component splitting and optimizes the component covariance so that the measurement nonlinearity is kept small, while minimizing the required number of components needed to have a good approximation of the prior.
Furthermore, we presented an algorithm for choosing the component size so that the nonlinearity is not too high, the resulting mixture is a good approximation of the prior, and the number of required components is minimized.
Now the goal is to find parameters for the BinoGM such that it is a good approximation of the prior, the nonlinearity is below a desired threshold η limit, and the number of components is minimized.
In this paper we have used
The network scores are computed according to the BIC approximation to the prior predictive distribution (Kass and Raftery 1995).
Then as, we make the approximation (8) Based on this approximation, the prior elicitation takes the form of an iterative algorithm, which begins at (which retains all edges in the model) and moves from to by removing a single edge from.
Nevertheless, by virtue of the normal prior distribution, normal approximation of the posterior function is useful.
The regularization parameter (related to the σ in the prior model) not only provides the weight of the prior model but also determines the validity of the linear approximation.
Prior to solving the problem, a rational approximation of the nonlinear restoring force function is applied to achieve a convergent truncation.
In our approach, the dark channel prior in[18, 20] is also used to improve the approximation of the atmosphere light.
