توضیحات
ABSTRACT
A useful de nition of \big data” is data that is too big to comfortably process on a single machine, either because of processor, memory, or disk bottlenecks. Graphics processing units can alleviate the processor bottleneck, but memory or disk bottlenecks can only be eliminated by splitting data across multiple machines. Communication between large numbers of machines is expensive (regardless of the amount of data being communicated), so there is a need for algorithms that perform distributed approximate Bayesian analyses with minimal communication. Consensus Monte Carlo operates by running a separate Monte Carlo algorithm on each machine, and then averaging individual Monte Carlo draws across machines. Depending on the model, the resulting draws can be nearly indistinguishable from the draws that would have been obtained by running a single machine algorithm for a very long time. Examples of consensus Monte Carlo are shown for simple models where single-machine solutions are available, for large single-layer hierarchical models, and for Bayesian additive regression trees (BART)
INTRODUCTION
This article describes a method of performing approximate Monte Carlo simulation from a Bayesian posterior distribution based on very large data sets. When the data are too large for a single processor, the obvious solution is to divide them among multiple processors. There are two basic methods of computing on the divided data. The rst is to divide the work among multiple cores on the same chip, either on a multi-core central processing unit (CPU), or on a massively parallel graphics processing unit (GPU)
Year : 2013
By : Steven L. Scott, Alexander W. Blocker, Fernando V. Bonassi, Hugh A. Chipman, Edward I. George, and Robert E. McCulloch
File Information : English Language / 22 Page/ Size : 2.8 M
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سال : 2013
کاری از : Steven L. Scott, Alexander W. Blocker, Fernando V. Bonassi, Hugh A. Chipman, Edward I. George, and Robert E. McCulloch
اطلاعات فایل : زبان انگلیسی /22 صفحه /حجم : 2.8 M
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