Variational message passing for skew t regression
Variational message passing for skew t regression
复制标题
偏斜回归的变分消息传递
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
M. Wand
中科院分区：
文献类型：
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作者：
Luca Maestrini;M. Wand
我们通过消息传递扩展了最近关于变分逼近的工作，以适应倾斜t回归模型的近似拟合和推断。由于非标准指数族的存在和需要数值积分，变分消息传递的推导是具有挑战性的。然而，因子图片段方法意味着只需要为特定的响应模型派生一次算法更新，该模型可以集成到任意复杂的模型中。我们工作的另一个优点是所有的倾斜t参数都是推断的，而不是保持固定的。此外，我们还证明了当使用简单的似然片段的辅助变量表示和方便的近似密度因式分解时，在偏t模型的辅助变量表示中产生的后验依赖可能导致变分消息传递近似的性能较差。©2018 John Wiley父子有限公司。
We extend recent work concerning variational approximations via message passing to accommodate approximate fitting and inference for skew t regression models. Derivation of variational message passing is challenging owing to the presence of non‐standard exponential families and numerical integration being needed. Nevertheless, the factor graph fragment approach means that algorithm updates only need to be derived once for a particular response model, which can be integrated in an arbitrarily complex model. Another advantage of our work is that all skew t parameters are inferred, rather than being held fixed. Furthermore, we show that posterior dependence arising in an auxiliary variable representation of a skew t model may lead to poor performances in terms of variational message passing approximation when using simple auxiliary variable representations of the likelihood fragment and convenient factorizations of the approximating densities. © 2018 John Wiley & Sons, Ltd.