Corpus ID: 88514184

A fully Bayesian strategy for high-dimensional hierarchical modeling using massively parallel computing

@article{Landau2016AFB,
  title={A fully Bayesian strategy for high-dimensional hierarchical modeling using massively parallel computing},
  author={Will Landau and Jarad Niemi},
  journal={arXiv: Computation},
  year={2016}
}
Markov chain Monte Carlo (MCMC) is the predominant tool used in Bayesian parameter estimation for hierarchical models. When the model expands due to an increasing number of hierarchical levels, number of groups at a particular level, or number of observations in each group, a fully Bayesian analysis via MCMC can easily become computationally demanding, even intractable. We illustrate how the steps in an MCMC for hierarchical models are predominantly one of two types: conditionally independent… Expand

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