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Computation and visualization of posterior densities in scalar nonlinear and non-Gaussian Bayesian filtering and smoothing problems
- Publication Year :
- 2017
-
Abstract
- One-dimensional Bayesian filtering and smoothing problems can be solved numerically using a number of algorithms, even in nonlinear and non-Gaussian cases. In this educational paper we advocate for the benefits of visualizing the obtained posterior densities as complement to, e.g., estimation error analysis. In addition to a review of Bayesian filtering and smoothing and the respective point mass and particle solutions, we devise a novel algorithm for filtering when the likelihood cannot be evaluated. Several instructive examples are discussed and easily adjustable matlab code is provided as complement to this paper.<br />Funding Agencies|project Scalable KaIman Filters - Swedish Research Council (VR)
Details
- Database :
- OAIster
- Notes :
- Roth, Michael, Gustafsson, Fredrik
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1234187762
- Document Type :
- Electronic Resource
- Full Text :
- https://doi.org/10.1109.ICASSP.2017.7953045