@inproceedings{1af6cff57fc449feb9da7b655e3e0d4e,
title = "Estimation of time-varying unknown nongaussian noise with DPM",
abstract = "Dirichlet process mixture (DPM) model, which is the state-of-the-art Bayesian nonparametric model, was introduced here to signal processing research field. In present Bayesian statistics it is used to model and inference random nongaussian distributions. We explored its ability to model and estimate nongaussian unknown stationary noise and our work will help dealing with problems in many fields of signal processing. Through some modifications, we also revealed its potential to model and estimate unknown nonstationary nongaussian noise. Sequential Monte Carlo based inference algorithm was developed to estimate time varying unknown nongaussian noise with DPM. Simulation results show the efficiency of our algorithm.",
author = "Bo Yang and Yuan, {Jian Ping} and Luo, {Jian Jun} and Yue, {Xiao Kui} and Ma, {Wei Hua}",
year = "2008",
doi = "10.1109/ICOSP.2008.4697123",
language = "英语",
isbn = "9781424421794",
series = "International Conference on Signal Processing Proceedings, ICSP",
pages = "272--275",
booktitle = "2008 9th International Conference on Signal Processing, ICSP 2008",
note = "2008 9th International Conference on Signal Processing, ICSP 2008 ; Conference date: 26-10-2008 Through 29-10-2008",
}