Accelerated Bayesian inference using deep learning
Accelerated Bayesian inference using deep learning
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DOI：
10.1093/mnras/staa1469
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发表时间：
2020-07-01
影响因子：
4.8
通讯作者：
Moss, Adam
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文献类型：
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作者：
Moss, Adam
我们提出了一种新的贝叶斯推理工具，使用神经网络（NN）参数化有效的马尔可夫链蒙特卡罗（MCMC）的建议。首先通过一系列非线性、可逆和非体积保持流将目标分布转换为对角、单位方差高斯分布。神经网络具有极强的表达能力，可以将复杂的目标转换为简单的潜在表示。然后可以在这个空间中提出有效的建议，我们在几个具有挑战性的分布上展示了高度的混合。参数空间可以自然地分成块对角速度层次，允许快速探索子空间，其中评估可能性是廉价的。使用这种方法，我们开发了一个嵌套的MCMC采样器进行贝叶斯推断和模型比较，发现高弯曲和多模态分析的可能性表现出色。我们还在普朗克2015年的数据上进行了测试，显示了精确的参数约束，并计算了在类似于20维参数空间中对标准宇宙学模型进行简单单参数扩展的证据。我们的方法对天文学和宇宙学中的一系列问题具有广泛的适用性，可从https://github.com/adammoss/nnest下载。
We present a novel Bayesian inference tool that uses a neural network (NN) to parametrize efficient Markov Chain Monte Carlo (MCMC) proposals. The target distribution is first transformed into a diagonal, unit variance Gaussian by a series of non-linear, invertible, and non-volume preserving flows. NNs are extremely expressive, and can transform complex targets to a simple latent representation. Efficient proposals can then be made in this space, and we demonstrate a high degree of mixing on several challenging distributions. Parameter space can naturally be split into a block diagonal speed hierarchy, allowing for fast exploration of subspaces where it is inexpensive to evaluate the likelihood. Using this method, we develop a nested MCMC sampler to perform Bayesian inference and model comparison, finding excellent performance on highly curved and multimodal analytic likelihoods. We also test it on Planck 2015 data, showing accurate parameter constraints, and calculate the evidence for simple one-parameter extensions to the standard cosmological model in similar to 20D parameter space. Our method has wide applicability to a range of problems in astronomy and cosmology and is available for download from https://github.com/adammoss/nnest.