کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1148512 | 1489754 | 2016 | 12 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: On photon statistics parametrized by a non-central Wishart random matrix On photon statistics parametrized by a non-central Wishart random matrix](/preview/png/1148512.png)
• Polykays of acting intensities modeled by a non-central Wishart random matrix are proposed as a new tool for computing photon statistics.
• The overall photocounting effect is introduced and analyzed, either for a deterministic number of pixels either for a random number of pixels.
• Generalized complete Bell polynomials are used in order to compute joint moments and joint cumulants of multivariate photocounters.
• Multivariate polykays can be successfully employed in order to approximate the multivariate Poisson–Mandel transform.
In order to tackle parameter estimation of photocounting distributions, polykays of acting intensities are proposed as a new tool for computing photon statistics. As unbiased estimators of cumulants, polykays are computationally feasible thanks to a symbolic method recently developed in dealing with sequences of moments. This method includes the so-called method of moments for random matrices and results to be particularly suited to deal with convolutions or random summations of random vectors. The overall photocounting effect on a deterministic number of pixels is introduced. A random number of pixels is also considered. The role played by spectral statistics of random matrices is highlighted in approximating the overall photocounting distribution when acting intensities are modeled by a non-central Wishart random matrix. Generalized complete Bell polynomials are used in order to compute joint moments and joint cumulants of multivariate photocounters. Multivariate polykays can be successfully employed in order to approximate the multivariate Poisson–Mandel transform. Open problems are addressed at the end of the paper.
Journal: Journal of Statistical Planning and Inference - Volume 169, February 2016, Pages 1–12