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众所周知的缺点，优点和贝叶斯模型计算的挑战：几个案例
Well-known shortcomings, advantages and computational challenges in Bayesian modelling: a few case stories |
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| 课程网址: |
http://videolectures.net/bark08_winther_wksaacc/
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| 主讲教师: |
Ole Winther
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| 开课单位: |
丹麦技术大学
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| 开课时间: |
2008-10-09
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| 课程语种: |
英语
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| 中文简介: |
贝叶斯推断可用于通过边际可能性来定量地判断数据拟合。在许多实际情况中，仅考虑一种模型，并且仅使用参数平均来避免过度拟合。我展示了基因组序列标签的大型数据集的这样一个例子，我们想要预测如果我们执行新的测序，我们将找到多少新的独特标签。使用了两个参数Yor-Pitman过程，结果说明了一些众所周知的事实：参数平均可能是至关重要的，大数据集将暴露模型的不足，如不切实际的狭窄误差条所示（交叉验证）预测。这表明我们应该提出更好的模型，并能够计算这些模型执行模型选择的边际可能性。在演讲的第二部分，我将讨论计算边际可能性的一些计算挑战。以高斯过程分类为例说明即使对于单模态后验，这也很难。
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| 课程简介: |
Bayesian inference can be used to judge the data fit quantitatively through the marginal likelihood. In many practical cases only one model is considered and parameter averaging is simply used to avoid overfitting. I show such an example for a large data set of genomic sequence tags where we want to predict how many new unique tags we will find if we perform new sequencing. The two parameter Yor-Pitman process is used and the results illustrate a few well-known facts: parameter averaging can be crucial and large data sets will expose the inadequacy of the model as seen by unrealistically narrow error-bars on (cross-validated) predictions. This indicates that we should come up with better models and being able to calculate the marginal likelihood for these models to perform model selection. In the second part of the talk I will discuss some of the computational challenges of calculating marginal likelihoods. Gaussian process classification is used as an example to illustrate that this is hard even for a uni-modal posterior.
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| 关 键 词: |
贝叶斯推理; 参数平均; 高斯过程分类
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-29：zyk
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| 阅读次数: |
35
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