High-dimensional ABC
D. J. Nott, V. M.-H. Ong, Y. Fan, S. A. Sisson
TL;DR
This chapter discusses extending Approximate Bayesian Computation (ABC) methods to high-dimensional problems, providing key ideas, concepts, and illustrative examples to address the challenges involved.
Contribution
It introduces the main ideas and concepts for applying ABC in high-dimensional settings, supported by examples and illustrations.
Findings
Highlights challenges of high-dimensional ABC
Provides illustrative examples of high-dimensional ABC
Summarizes key concepts for high-dimensional ABC
Abstract
This Chapter, "High-dimensional ABC", is to appear in the forthcoming Handbook of Approximate Bayesian Computation (2018). It details the main ideas and concepts behind extending ABC methods to higher dimensions, with supporting examples and illustrations.
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Taxonomy
TopicsMarkov Chains and Monte Carlo Methods