Bonomi, Massimiliano Camilloni, Carlo Cavalli, Andrea Vendruscolo, Michele 2016-01-22 Modeling a complex system is almost invariably a challenging task. The incorporation of experimental observations can be used to improve the quality of a model and thus to obtain better predictions about the behavior of the corresponding system. This approach, however, is affected by a variety of different errors, especially when a system simultaneously populates an ensemble of different states and experimental data are measured as averages over such states. To address this problem, we present a Bayesian inference method, called “metainference,” that is able to deal with errors in experimental measurements and with experimental measurements averaged over multiple states. To achieve this goal, metainference models a finite sample of the distribution of models using a replica approach, in the spirit of the replica-averaging modeling based on the maximum entropy principle. To illustrate the method, we present its application to a heterogeneous model system and to the determination of an ensemble of structures corresponding to the thermal fluctuations of a protein molecule. Metainference thus provides an approach to modeling complex systems with heterogeneous components and interconverting between different states by taking into account all possible sources of errors. application/pdf https://n2t.net/ark:/12658/srd1319103 https://susi.usi.ch/global/documents/319103 https://susi.usi.ch/documents/319103/files/Bonomi_SA_2016.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.1126/sciadv.1501177 info:eu-repo/semantics/altIdentifier/ark/12658/srd1319103 info:eu-repo/semantics/openAccess CC BY-NC Science advances. - 2016, vol. 2, no. 1, p. e1501177 Statistical inference Structural biology Maximum entropy principle info:eu-repo/classification/udc/61 Metainference : a Bayesian inference method for heterogeneous systems http://purl.org/coar/resource_type/c_6501