Sequentially Adaptive Bayesian Learning Algorithms for Inference and Optimization

 Sequentially Adaptive Bayesian Learning Algorithms for Inference and Optimization. Garland Durham and John Geweke, 2015.

The sequentially adaptive Bayesian learning algorithm is an extension and combination of sequential particle filters for a static target and simulated annealing. A key distinction between SABL and these approaches is that the introduction of information in SABL is adaptive and controlled, with control guaranteeing that the algorithm performs reliably and efficiently in a wide variety of settings without any specific further attention. This avoids the need for tedious tuning, tinkering, trial and error. The algorithm is pleasingly parallel and when executed using one or more graphics processing units is much faster than competing algorithms, many of which are not pleasingly parallel and unable to exploit the massively parallel architecture of GPUs. This paper describes the algorithm, provides theoretical foundations more self-contained than those in the existing literature, provides applications to Bayesian inference and optimization problems illustrating many advantages of the algorithm, and briefly describes the nonproprietary SABL software.

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