matlab概率分布拟合
这可能是一个愚蠢的问题！我有一个数组 P，它表示某些数据的概率分布，例如 [0;0.3;0.7] 如何确定 P 的离散概率分布的类型或类别？原始数据我无法获取。
dfittool 或 fitdist 要求我提供数据作为输入，而我已经有了它的概率分布。有什么想法吗？
This might be a silly question! I have a array P which represents the probability distribution of some data e.g. [0;0.3;0.7] How can I determine the type or class of discrete probability distribution of P? The original data is unavailable to me.
dfittool or fitdist requires me to give the data as input, while I already have its probability distribution. Any ideas?
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您可能在讲座或阅读过程中看到过不同的概率分布。您所要做的就是根据候选人绘制给定的分布。由于分布本身是参数化的，因此曲线拟合或试验结束误差就会发挥作用。误差最小、最适合的分布可能就是您正在寻找的分布。
You probably might have seen different probability distributions during lecture or your reading. All you have to do is plotting the given distribution against the candidates. As the distributions itself are parametrized, curve fitting or trial end error come into play. The distribution with the least error, best fit, might be the one you are looking for.
不可能先验地找出某些数据（尤其是在您的示例中 n 如此低）来自哪种分布。
如果您了解生成数据的过程，您也许能够了解要测试哪些分布。也许您的数据来自伽马分布族，也许您的数据来自威布尔分布族等等。然后，您可以拟合这些一般分布，看看它们是否有可能简化为更常见的分布。
要直观地表示数据如何近似某个分布，您可以使用 PROBPLOT 。
一旦确定了可能的分布，您就可以将它们拟合到数据并使用贝叶斯信息准则 (BIC) 比较哪种拟合最能描述数据。请注意，除非您拥有大量无噪声数据，否则如果您有多个 BIC 相对较低的可能分布，则无法判断哪种拟合是正确的。
It is not possible to find out a priori what kind of distribution some data (especially with as low n as in your example) is coming from.
If you have an idea of the process that generated your data, you might be able to get an idea of which distributions to test. Maybe your data comes from the family of gamma distributions, maybe your data comes from the family of Weibull distributions etc. Then, you can fit these general distributions and see whether they are likely to simplify to a more common distribution.
For a visual representation of how well your data could approximate a certain distribution, you can use PROBPLOT.
Once you have identified possible distributions, you can fit them to the data and use the Bayesian Information Criterion (BIC) to compare which fit describes the data best. Note that unless you have huge numbers of noise-free data, it is impossible to tell which fit is correct if you have several possible distributions with comparatively low BIC.