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B0578
Title: Scalable Bayesian inference for time series via divide-and-conquer Authors:  David Dunson - Duke University (United States)
Deborshee Sen - University of Bath (United Kingdom) [presenting]
Abstract: Bayesian computational algorithms tend to scale poorly as data size increases. This had led to the development of divide-and-conquer-based approaches for scalable inference. These divide the data into subsets, perform inference for each subset in parallel, and then combine these inferences. While appealing theoretical properties and practical performance have been demonstrated for independent observations, scalable inference for dependent data remains challenging. We study the problem of Bayesian inference from very long time series. The literature focuses mainly on approximate approaches that lack any theoretical guarantees and may provide arbitrarily poor accuracy in practice. We propose a simple and scalable divide-and-conquer method, and provide accuracy guarantees. Numerical simulations and real data applications demonstrate the effectiveness of our approach.