You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: spm_SARS_priors_B.m
    path of file: ./repos/spm/toolbox/DEM
    
    the code of the file until where you have to start completion: function [P,C,str] = spm_SARS_priors_B(nN)
% Generate prior expectation and covariance log parameters (bound version)
% FORMAT [pE,pC,str] = spm_SARS_priors_B(nN)
%
% nN          - number of age groups
%
% pE          - prior expectation (structure)
% pC          - prior covariances (structure)
% str.factor  - latent or hidden factors

    
function [P,C,str] = spm_SARS_priors_B(nN)
% Generate prior expectation and covariance log parameters (bound version)
% FORMAT [pE,pC,str] = spm_SARS_priors_B(nN)
%
% nN          - number of age groups
%
% pE          - prior expectation (structure)
% pC          - prior covariances (structure)
% str.factor  - latent or hidden factors
% str.factors - levels of each factor
% str.outcome - outcome names (see spm_SARS_gen)
% str.names   - parameter names
% str.field   - field names of random effects
% rfx         - indices of random effects
%
% This routine assembles the (Gaussian) and priors over the parameters of a
% generative model for SARS-19. This generative model is based upon a mean
% field approximation to ensemble of population dynamics, in which four
% marginal distributions are coupled through probability transition
% matrices. The marginal distributions correspond to 4 factors; namely,
% location, infection, symptom and testing (LIST) states. The parameters of
% this model determine the initial (probability) states and the transitions
% among the states that show certain conditional independ