Maximum a posterior linear regression with elliptically symmetric matrix variate priors

@inproceedings{Chou1999MaximumAP,
  title={Maximum a posterior linear regression with elliptically symmetric matrix variate priors},
  author={Wu Chou},
  booktitle={EUROSPEECH},
  year={1999}
}
  • Wu Chou
  • Published 1999 in EUROSPEECH
In this paper, elliptic symmetric matrix variate distribution is proposed as the prior distribution for maximum a posterior linear regression (MAPLR) based model adaptation. The exact close form solution of MAPLR with elliptically symmetric matrix variate priors is obtained. The effects of the proposed prior in MAPLR are characterized and compared with conventional maximum likelihood linear regression (MLLR). The proposed priors are significant informative priors, through which a well-founded… CONTINUE READING
Highly Influential
This paper has highly influenced 14 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 98 citations. REVIEW CITATIONS

1 Figure or Table

Topics

Statistics

01020'01'03'05'07'09'11'13'15'17
Citations per Year

98 Citations

Semantic Scholar estimates that this publication has 98 citations based on the available data.

See our FAQ for additional information.