PRML第9章高斯混合模型


PRML第434页,用极大似然估计高斯混合模型的参数时候,会出现singularity的问题。但在单变量高斯分布时候不会, 文中是这样说的:'To understand the difference, note that if a single Gaussian collapses onto a data point it will contribute multiplicative factors to the likelihood function arising from the other data points and these factors will go to zero exponentially fast, giving an overall likelihood that goes to zero rather than infinity.'
如何理解其中的趋向于0呢?
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