摘要翻译：
当存在一个以上同样可信的数据解释时，噪声数据的最佳解释会发生什么？在贝叶斯模型学习框架中，答案依赖于从数据中推断出的模型参数的动力学的先验期望。先验的当地时间限制不足以选择一种解释而不是另一种解释。另一方面，由先验的$1/f$噪声谱引起的非局部时间约束被证明允许学习特定的模型参数，即使对数据有无限多个同样可信的解释。这种转变是通过模型估计问题到耗散物理系统的显着映射来推断的，允许使用强大的统计力学方法来揭示从不确定到确定模型学习的转变。
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英文标题：
《Ambiguous model learning made unambiguous with 1/f priors》
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作者：
Gurinder Singh Atwal, William Bialek
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最新提交年份：
2005
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Quantitative Biology        数量生物学
二级分类：Neurons and Cognition        神经元与认知
分类描述：Synapse, cortex, neuronal dynamics, neural network, sensorimotor control, behavior, attention
突触，皮层，神经元动力学，神经网络，感觉运动控制，行为，注意
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英文摘要：
  What happens to the optimal interpretation of noisy data when there exists more than one equally plausible interpretation of the data? In a Bayesian model-learning framework the answer depends on the prior expectations of the dynamics of the model parameter that is to be inferred from the data. Local time constraints on the priors are insufficient to pick one interpretation over another. On the other hand, nonlocal time constraints, induced by a $1/f$ noise spectrum of the priors, is shown to permit learning of a specific model parameter even when there are infinitely many equally plausible interpretations of the data. This transition is inferred by a remarkable mapping of the model estimation problem to a dissipative physical system, allowing the use of powerful statistical mechanical methods to uncover the transition from indeterminate to determinate model learning.
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PDF链接：
https://arxiv.org/pdf/q-bio/0512040