Locally Adaptive Smoothing with Markov Random Fields and Shrinkage Priors.

ABSTRACT: We present a locally adaptive nonparametric curve fitting method that operates within a fully Bayesian framework. This method uses shrinkage priors to induce sparsity in order-k differences in the latent trend function, providing a combination of local adaptation and global control. Using a scale mixture of normals representation of shrinkage priors, we make explicit connections between our method and kth order Gaussian Markov random field smoothing. We call the resulting processes shrinkage prior Markov random fields (SPMRFs). We use Hamiltonian Monte Carlo to approximate the posterior distribution of model parameters because this method provides superior performance in the presence of the high dimensionality and strong parameter correlations exhibited by our models. We comp

SUBMITTER: Faulkner JR 

PROVIDER: S-EPMC5942601 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature