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# Robust Sequential Learning Algorithms for Linear Observation Models

@article{Deng2007RobustSL, title={Robust Sequential Learning Algorithms for Linear Observation Models}, author={Guang Deng}, journal={IEEE Transactions on Signal Processing}, year={2007}, volume={55}, pages={2472-2485} }

- Published 2007 in IEEE Transactions on Signal Processing
DOI:10.1109/TSP.2007.893733

This paper presents a study of sequential parameter estimation based on a linear non-Gaussian observation model. To develop robust algorithms, we consider a family of heavy-tailed distributions that can be expressed as the scale mixture of Gaussian and extend the development to include some robust penalty functions. We treat the problem as a Bayesian learning problem and develop an iterative algorithm by using the Laplace approximation for the posterior and the minorization-maximization (MM… CONTINUE READING