Nonparametric learning from Bayesian models with randomized objective functions
Nonparametric learning from Bayesian models with randomized objective functions
复制标题
具有随机目标函数的贝叶斯模型的非参数学习
DOI：
10.1017/cbo9780511752834.007
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
C. Holmes
中科院分区：
文献类型：
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作者：
Simon Lyddon;S. Walker;C. Holmes
贝叶斯学习建立在一个假设上，即模型空间包含数据生成机制的真实反映。这种假设是有问题的，特别是在复杂的数据环境中。在这里，我们提出了一种贝叶斯非参数学习方法，它利用统计模型，但不假设模型是正确的。我们的方法具有比使用参数模型更好的特性，并且允许蒙特卡罗采样方案，可以在现代计算机体系结构上提供大规模的可扩展性。当样本量较小时，学习的基于模型的方面对于正则化非参数推理以及校正近似方法（如变分贝叶斯（VB））特别有吸引力。我们用VB分类器和贝叶斯随机森林等一些例子来演示这种方法。
Bayesian learning is built on an assumption that the model space contains a true reflection of the data generating mechanism. This assumption is problematic, particularly in complex data environments. Here we present a Bayesian nonparametric approach to learning that makes use of statistical models, but does not assume that the model is true. Our approach has provably better properties than using a parametric model and admits a Monte Carlo sampling scheme that can afford massive scalability on modern computer architectures. The model-based aspect of learning is particularly attractive for regularizing nonparametric inference when the sample size is small, and also for correcting approximate approaches such as variational Bayes (VB). We demonstrate the approach on a number of examples including VB classifiers and Bayesian random forests.
DOI：
10.1111/j.2044-8317.2011.02037.x
发表时间：
2013-02
期刊：
The British journal of mathematical and statistical psychology
影响因子：
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作者：
Gelman A;Shalizi CR
通讯作者：
Shalizi CR