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Robust Inference for State-Space Models with Skewed Measurement Noise

Citation Author(s):
Henri Nurminen, Tohid Ardeshiri, Robert Piche, Fredrik Gustafsson
Submitted by:
Henri Nurminen
Last updated:
19 March 2016 - 12:12pm
Document Type:
Poster
Document Year:
2016
Event:
Presenters Name:
Henri Nurminen

Abstract 

Abstract: 

Filtering and smoothing algorithms for linear discrete-time state-space models with skewed and heavy-tailed measurement noise are presented. The algorithms use a variational Bayes approximation of the posterior distribution of models that have normal prior and skew-t-distributed measurement noise.

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ICASSP2016_poster.pdf

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