A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2006; you can also visit the original URL.
The file type is application/pdf
.
Multiplicative Algorithms for Maximum Penalized Likelihood Inversion with Non Negative Constraints and Generalized Error Distributions
2006
Communications in Statistics - Theory and Methods
In many linear inverse problems the unknown function f (or its discrete approximation θ p×1 ), which needs to be reconstructed, is subject to the nonnegative constraint(s); we call these problems the nonnegative linear inverse problems (NNLIPs). This paper considers NNLIPs. However, the error distribution is not confined to the traditional Gaussian or Poisson distributions. We adopt the exponential family of distributions where Gaussian and Poisson are special cases. We search for the
doi:10.1080/03610920500501478
fatcat:rebbtkuwhjbozk2qdcwppqt6hy