1,837 research outputs found
Robust Bayesian Filtering Using Bayesian Model Averaging and Restricted Variational Bayes
Bayesian filters can be made robust to outliers if
the solutions are developed under the assumption of heavy-tailed
distributed noise. However, in the absence of outliers, these robust
solutions perform worse than the standard Gaussian assumption
based filters. In this work, we develop a novel robust filter that
adopts both Gaussian and multivariate t-distributions to model
the outliers contaminated measurement noise. The effects of these
distributions are combined within a Bayesian Model Averaging
(BMA) framework. Moreover, to reduce the computational complexity
of the proposed algorithm, a restricted variational Bayes
(RVB) approach handles the multivariate t-distribution instead
of its standard iterative VB (IVB) counterpart. The performance
of the proposed filter is compared against a standard cubature
Kalman filter (CKF) and a robust CKF (employing IVB method)
in a representative simulation example concerning target tracking
using range and bearing measurements. In the presence of
outliers, the proposed algorithm shows a 38% improvement
over CKF in terms of root-mean-square-error (RMSE) and is
computationally 2.5 times more efficient than the robust CKF
Dynamic Bayesian Combination of Multiple Imperfect Classifiers
Classifier combination methods need to make best use of the outputs of
multiple, imperfect classifiers to enable higher accuracy classifications. In
many situations, such as when human decisions need to be combined, the base
decisions can vary enormously in reliability. A Bayesian approach to such
uncertain combination allows us to infer the differences in performance between
individuals and to incorporate any available prior knowledge about their
abilities when training data is sparse. In this paper we explore Bayesian
classifier combination, using the computationally efficient framework of
variational Bayesian inference. We apply the approach to real data from a large
citizen science project, Galaxy Zoo Supernovae, and show that our method far
outperforms other established approaches to imperfect decision combination. We
go on to analyse the putative community structure of the decision makers, based
on their inferred decision making strategies, and show that natural groupings
are formed. Finally we present a dynamic Bayesian classifier combination
approach and investigate the changes in base classifier performance over time.Comment: 35 pages, 12 figure
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