Informative and adaptive distances and summary statistics in sequential approximate Bayesian computation.

ABSTRACT: Calibrating model parameters on heterogeneous data can be challenging and inefficient. This holds especially for likelihood-free methods such as approximate Bayesian computation (ABC), which rely on the comparison of relevant features in simulated and observed data and are popular for otherwise intractable problems. To address this problem, methods have been developed to scale-normalize data, and to derive informative low-dimensional summary statistics using inverse regression models of parameters on data. However, while approaches only correcting for scale can be inefficient on partly uninformative data, the use of summary statistics can lead to information loss and relies on the accuracy of employed methods. In this work, we first show that the combination of adaptive scale normalization

SUBMITTER: Schalte Y 

PROVIDER: S-EPMC10202307 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature