Index of papers in April 2015 that mention
• AIC
Lorenza A. D’Alessandro, Regina Samaga, Tim Maiwald, Seong-Hwan Rho, Sandra Bonefas, Andreas Raue, Nao Iwamoto, Alexandra Kienast, Katharina Waldow, Rene Meyer, Marcel Schilling, Jens Timmer, Steffen Klamt, Ursula Klingmüller
 Ordinary differential equation model selection A comparable ranking of all models was obtained utilizing the Akaike Information Criterion ( AIC ) (S6 Fig). Ordinary differential equation modeling First, the Akaike Information Criterion ( AIC ) is defined as Ordinary differential equation modeling In this work, we present all rankings with unprocessed -210g(L) values, AIC ranking and LRT for all model structures against the complete model. Ordinary differential equation modeling While the AIC allows the creation of a complete ranking and therefore a comparison of different candidate models against each other, the LRT provides us with more detailed information regarding the pairwise comparison of a nested model against the null model. Supporting Information ODE model selection according to the Akaike Information Criterion ( AIC ). Supporting Information The Akaike Information Criterion ( AIC ) has been utilized to penalize the likelihood.
AIC is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Thomas W. Spiesser, Clemens Kühn, Marcus Krantz, Edda Klipp
 Parameter estimation To find the model version that would best approximate reality given the data and the number of parameters we employed the Akaike Information Criterion ( AIC ) to rank the models [46]. Parameter estimation The AIC establishes a relationship between the maximum likelihood and the Kullback-Leibler information, which is a measure for the information lost when approximating reality with a model [62]. Parameter estimation The AIC was computed as Results To test which model is more suitable to describe the given data, we ranked them using the Akaike Information Criterion ( AIC ) [46]. Supporting Information The values of the best fit and the average over 100 fits are given for the objective value (WRSS), the log-likelihood (ln(L( p ) )) and the Akaike Information Criterion ( AIC ), as defined in Materials and Methods.
AIC is mentioned in 6 sentences in this paper.
Topics mentioned in this paper: