arg "" help="The key for the config"
更棘手的是，手术后仍有8.1%-18.6%的5年复发风险，宫颈结构改变还可能导致随访漏诊，二次手术难度大幅增加。
，更多细节参见谷歌浏览器下载
不稳定训练不改善平衡。平衡能力随力量增强而提升，与训练表面是否稳定无直接关联。
Imagine you are a retail company, and you want to generate synthetic data representing your sales orders, based on historical data. A rather difficult aspect of this is how to geographically distribute the synthetic data. The simplest approach is just to sample a random location (say a postal code) for each order, based on how frequent similar orders were in the past. For now, similar might just mean of the same category, or sold in the same channel (in-store, online, etc.) A frequentist approach to this problem usually starts by clustering historical data based on the grouping you chose and estimate the distribution of postal codes for each cluster using the counts of sales in the data. If you normalize the counts by category, you get a conditional probability distribution P(postal code∣category)P(\text{postal code} | \text{category})P(postal code∣category) which you can then sample from.