"for prior information" is a correct and usable phrase in written English.
It is often used to introduce additional context or background information that is relevant to the main topic being discussed. Example: "For prior information, it is important to note that the company experienced a similar financial crisis in 2008." This sentence suggests that the prior information mentioned is necessary to understand the current situation or decision being made.
Because it is not staging the event itself, co-director Richard Valentine and his team will be at the mercy of the concert organisers for prior information on the set list and running order.
Good generation characteristics without the need for prior information have allowed S-systems to become one of the most promising canonical models.
That is to say, for sparser signal, ASS can exploit more signal structure information as for prior information and vice versa.
In addition, the empirical modelling approach enables confidence bounds on the predicted GWLs and SGI values to be estimated without the need for prior information about catchment or aquifer parameters.
Previous authors have suggested dynamic encoding mechanisms for prior information, whereby top-down modulation of firing patterns on a trial-by-trial basis creates short-term representations of priors.
Using this approach, we can report a p-value which is adjusted for prior information for each variant.
Bayesian methods for linkage analysis are useful because they allow for incorporation of prior information about allele frequencies, meiotic drive, and other factors important to linkage calculations.
Markov chain Monte Carlo Bayesian estimation offers a robust and statistically rigorous approach for incorporation of prior information into mechanism-based models.
Taking advantage of prior information for the purpose of enhancing the performance of NMF has been widely investigated.
Several methods are available for assigning prior information to the distribution of countable networks for a given set of genes.
Bayesian methods have the advantage of allowing for the introduction of co-variates in the model, and for embodying prior information [ 27].
