Title
Successive synthesis of latent Gaussian trees
Document Type
Conference Proceeding
Publication Date
12-7-2017
Abstract
A new synthesis scheme is proposed to effectively generate a random vector with prescribed joint density that induces a (latent) Gaussian tree structure. The quality of synthesis is measured by total variation distance between the synthesized and desired statistics. The proposed layered and successive encoding scheme relies on the learned structure of tree to use minimal number of common random variables to synthesize the desired density. We characterize the achievable rate region for the rate tuples of multi-layer latent Gaussian tree, through which the number of bits needed to simulate such Gaussian joint density are determined. The random sources used in our algorithm are the latent variables at the top layer of tree, the additive independent Gaussian noises, and the Bernoulli sign inputs that capture the ambiguity of correlation signs between the variables.
Publication Source (Journal or Book title)
Proceedings - IEEE Military Communications Conference MILCOM
First Page
315
Last Page
320
Recommended Citation
Moharrer, A., Wei, S., Amariucai, G., & Deng, J. (2017). Successive synthesis of latent Gaussian trees. Proceedings - IEEE Military Communications Conference MILCOM, 2017-October, 315-320. https://doi.org/10.1109/MILCOM.2017.8170791