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BAMBI: blind accelerated multimodal Bayesian inference
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DOI：10.1111/j.1365-2966.2011.20288.x.png)
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摘要
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In this paper, we present an algorithm for rapid Bayesian analysis that combines the benefits of nested sampling and artificial neural networks (NNs). The blind accelerated multimodal Bayesian inference (BAMBI) algorithm implements the MULTINEST package for nested sampling as well as the training of an artificial NN to learn the likelihood function. In the case of computationally expensive likelihoods, this allows the substitution of a much more rapid approximation in order to increase significantly the speed of the analysis. We begin by demonstrating, with a few toy examples, the ability of an NN to learn complicated likelihood surfaces. BAMBI's ability to decrease running time for Bayesian inference is then demonstrated in the context of estimating cosmological parameters from Wilkinson Microwave Anisotropy Probe and other observations. We show that valuable speed increases are achieved in addition to obtaining NNs trained on the likelihood functions for the different model and data combinations. These NNs can then be used for an even faster follow-up analysis using the same likelihood and different priors. This is a fully general algorithm that can be applied, without any pre-processing, to other problems with computationally expensive likelihood functions.
Keyword:
methods: data analysis
methods: statistical
cosmological parameters
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期刊
IF:
4.8
论文数:
7.0W
被引数:
25.0W
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Multimodal nested sampling: an efficient and robust alternative to Markov Chain Monte Carlo methods for astronomical data analyses多模态嵌套采样: 用于天文数据分析的马尔可夫链蒙特卡洛方法的有效且稳健的替代方法OAAI
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