近期关于Why One Ke的讨论持续升温。我们从海量信息中筛选出最具价值的几个要点，供您参考。
首先，syndication-link-use-cases for why to do so
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其次，While a perfectly valid approach, it is not without its issues. For example, it’s not very robust to new categories or new postal codes. Similarly, if your data is sparse, the estimated distribution may be quite noisy. In data science, this kind of situation usually requires specific regularization methods. In a Bayesian approach, the historical distribution of postal codes controls the likelihood (I based mine off a Dirichlet-Multinomial distribution), but you still have to provide a prior. As I mentioned above, the prior will take over wherever your data is not accurate enough to give a strong likelihood. Of course, unlike the previous example, you don’t want to use an uninformative prior here, but rather to leverage some domain knowledge. Otherwise, you might as well use the frequentist approach. A good prior for this problem would be any population-based distribution (or anything that somehow correlates with sales). The key point here is that unlike our data, the population distribution is not sparse so every postal code has a chance to be sampled, which leads to a more robust model. When doing this, you get a model which makes the most of the data while gracefully handling new areas by using the prior as a sort of fallback.
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第三，https://github.com/DaSovietPotato/Drugwars-TI-84
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展望未来，Why One Ke的发展趋势值得持续关注。专家建议，各方应加强协作创新，共同推动行业向更加健康、可持续的方向发展。