(Room 513)


In recent years, there have been ever-increasing demands for data-intensive scientific research. Routine use of digital sensors, high throughput experiments, and intensive computer simulations have created a data deluge imposing new challenges on scientific communities that attempt to process and analyze such data. This is especially challenging for scientific studies that involve Bayesian methods, which typically require computationally intensive Monte Carlo algorithms for their implementation. As a result, although Bayesian methods provide a robust and principled framework for analyzing data, their relatively high computational cost for Big Data problems has limited their application. The objective of this workshop is to discuss the advantages of Bayesian inference in the age of Big Data and to introduce new scalable Monte Carlo methods that address computational challenges in Bayesian analysis. This is a follow up to our recent workshop on Bayesian Inference for Big Data at Oxford University: BIBiD 2015. It will consist of invited talks and a poster session. Topics of interest include (but are not limited to):


The workshop will take place in room 513

(*) Panelists: Emily Fox, David Blei, Anthony Lee, Ryan Adams, Andrew Duncan, Arthur Gretton, Iain Murray (U. Edinburgh), Michael Betancourt (U. Warwick), Yee Whye Teh (U. Oxford), Max Welling (chair).


  • Importance sampling with hamiltonian dynamics
  • Can random projections replace uniform subsampling in MCMC for linear regression of tall datasets?
  • Efficient MCMC for Gibbs random fields using pre-computation
  • DiLeMMA - Distributed learning with Markov Chain Monte Carlo algorithms
  • Provable bayesian inference via particle mirror descent
  • Beyond worst-case mixing times for Markov chains
  • Efficient bayesian model selection via stochastic gradient MCMC
  • Nonreversible stochastic gradient Langevin dynamics
  • Kernel adaptive sequential Monte Carlo
  • Subsampling-based approximate Monte Carlo for discrete distributions
  • Measuring sample quality with Stein’s method,
  • Big Bayes with no sub-sampling bias: Paths of Partial Posteriors
  • Yang Yang (U. Minnesota Twin Cities)
  • A. Taylan Cemgil, Alper K. Bozkurt (Bogazici U.), Kari Heine (UC. London) and Nick Whiteley (U. Bristol)
  • Details

    The workshop will be held at NIPS 2015 in Montreal on the 12th of December. There will be invited speakers and contributed posters for which you can submit an abstract until the 10th of October.

    The workshop is endorsed by the International Society for Bayesian Analysis (ISBA).


    The following people are involved in organizing this workshop:

    Previous Workshop

    This is a follow up to our recent workshop at the University of Oxford: BIBiD2015.