Abstract / truncated to 115 words (read the full abstract)

System designers across all disciplines of technology face the need to develop machines capable of independently processing and analyzing data and predicting future data. This is the fundamental problem of interest in “estimation theory,” wherein probabilistic analyses are used to isolate relationships between variables, and in “statistical inference,” wherein those variables are used to make inferences about real-world quantities. In practice, all estimators are designed based on limited statistical generalizations about the behavior of the observed and latent variables of interest; however, these models are rarely fully representative of reality. In such cases, there exists a “model misspecification,” and the resulting estimators will produce results that differ from those of the properly specified estimators. Evaluating ... toggle 6 keywords

bayesian statistics – bounds analysis – dynamic state estimation – estimation theory – gaussian filters – model misspecification

Follow @eurasip