Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke





.......................

.......................


Publications [#329109] of David B. Dunson

search arxiv.org.

Papers Published

  1. Li, C; Srivastava, S; Dunson, DB, Simple, scalable and accurate posterior interval estimation, Biometrika, vol. 104 no. 3 (September, 2017), pp. 665-680, Oxford University Press (OUP) [doi]
    (last updated on 2019/05/23)

    Abstract:
    © 2017 Biometrika Trust. Standard posterior sampling algorithms, such as Markov chain Monte Carlo procedures, face major challenges in scaling up to massive datasets. We propose a simple and general posterior interval estimation algorithm to rapidly and accurately estimate quantiles of the posterior distributions for one-dimensional functionals. Our algorithm runs Markov chain Monte Carlo in parallel for subsets of the data, and then averages quantiles estimated from each subset. We provide strong theoretical guarantees and show that the credible intervals from our algorithm asymptotically approximate those from the full posterior in the leading parametric order. Our algorithm has a better balance of accuracy and efficiency than its competitors across a variety of simulations and a real-data example.

 

dept@math.duke.edu
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320