Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization

@article{Rangan2015InferenceFG,
  title={Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization},
  author={S. Rangan and A. Fletcher and Philip Schniter and Ulugbek S. Kamilov},
  journal={IEEE Transactions on Information Theory},
  year={2015},
  volume={63},
  pages={676-697}
}
  • S. Rangan, A. Fletcher, +1 author Ulugbek S. Kamilov
  • Published 2015
  • Computer Science, Mathematics
  • IEEE Transactions on Information Theory
  • Generalized linear models, where a random vector x is observed through a noisy, possibly nonlinear, function of a linear transform $ \mathrm {z}= \mathrm {A} \mathrm {x} $ , arise in a range of applications in nonlinear filtering and regression. Approximate message passing (AMP) methods, based on loopy belief propagation, are a promising class of approaches for approximate inference in these models. AMP methods are computationally simple, general, and admit precise analyses with testable… CONTINUE READING
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