For dynamic systems, sequential Bayesian estimation requires updating of the filtering and predictive densities. For nonlinear and non-Gaussian models, sequential updating is not as straightforward as in the linear Gaussian model. In this paper, densities are approximated as finite mixture models as is done in the Gaussian sum filter. A novel method is presented, whereby sequential updating of ...

We introduce a new sequential algorithm for making robust predictions in the presence of changepoints. Unlike many previous approaches [1], which focus on the problem of detecting and locating changepoints, our algorithm focuses on the problem of making predictions even when such changes might be present. We introduce nonstationary covariance functions to be used in Gaussian process prediction ...

Pattern reordering is proposed as an alternative to sequential and batch processing for online category learning. Upon detecting that the categorization of a new input pattern is ambiguous, the input is postponed for a predefined time, after which it is reexamined and categorized for good. This approach is shown to improve the categorization performance over purely sequential processing, while ...

The sequential testing of more than two hypotheses has important applications in direct-sequence spread spectrum signal acquisition, multiple-resolution-element radar, and other areas. A useful sequential test which we term the MSPRT is studied in this paper. The test is shown to be a generalization of the Sequential Probability Ratio Test. Under Bayesian assumptions, it is argued that the MSPR...

Understanding the spatial soil salinity aids farmers and researchers in identifying areas in the field where special management practices are required. Apparent electrical conductivity measured by electromagnetic induction instrument in a fairly quick manner has been widely used to estimate spatial soil salinity. However, methods used for this purpose are mostly a series of interpolation algori...