cmb_estimation

 [optimal, gradient_V, init, time] = cmb_estimation(network, q_info, bounds, data, prior, preposterior, pp, V, cmb_options)

 gradient_V Gradient of enzyme posterior loss with respect to fluxes (currently not computed)
  
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 Initial guesses for X and q (where preposterior q means: using prior (and possibly data) for q only 
 
 X_init = argmin_X sum(sum( [ [X - preposterior.X.mean] ./ preposterior.X.std ] .^2));  WITHIN allowed polytopes!
 q_init = argmin_X sum(sum( [ [q - preposterior.q.mean] ./ preposterior.q.std     ] .^2);   WITHIN allowed ranges!
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 choice of initial values depends on cmb_options.initial_values_variant:
  'average_sample', 
  'preposterior_mode', 
  'random'
  'true_values'
  'given_values'