Scalable Monte Carlo Methods for Bayesian Analysis of Big Data

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 (BIBiD 2015) at Oxford University (https://github.com/BigBayes/bigbayes.github.io/wiki/BIBiD-2015). It will consist of invited and contributed talks, poster spotlights, and a poster session. There will be a panel discussion on "Bayesian inference for Big Data" at the end of the session. Topics of interest include (but are not limited to):
• Advantages of Bayesian methods in the age of Big Data
• Distributed/parallel Markov Chain Monte Carlo (MCMC)
• MCMC using mini-batches of data
• MCMC using surrogate functions
• MCMC using GPU computing
• Precomputing strategies 
• MCMC and variational methods
• Geometric methods in sampling algorithms
• Hamiltonian Monte Carlo
• Sequential Monte Carlo
This workshop has been endorsed by ISBA. Young researchers participating in the workshop can apply for an ISBA special Travel Award.