Distribution Fitter for Jupyter/Scipy/Python

• Using scipy for data fitting
  https://education.molssi.org/python-data-analysis/03-data-fitting/index.html

• scipy.stats.fit
  https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fit.html

• scipy.stats
  https://docs.scipy.org/doc/scipy/reference/stats.html

• fitter:
  这是个集成package, 代只有三个代码源文件，实现上用的是“scipy.stats”和“PyData”系列
  https://fitter.readthedocs.io/en/latest/tuto.html

• reliability reliability engineering
  https://reliability.readthedocs.io/en/latest

Distribution Fitter of Matlab

Fit probability distributions to data:
https://ww2.mathworks.cn/help/stats/distributionfitter-app.html?lang=en
https://ww2.mathworks.cn/help/stats/distributionfitter.html

说明

distributionFitter(分布拟合器): 交互式对 导入 MATLAB® 工作区 的 数据 进行 概率分布拟合.
您可以从 22 个内置概率分布集合进行选择，也可以创建您自己的自定义分布.
该 App 在数据直方图上叠加显示拟合分布图。
可用的绘图包括：

• PDF(概率密度函数)、
• CDF(累积分布函数 )、
• 概率图
• 生存函数

您可以将拟合的参数值作为概率分布对象导出到工作区，并使用对象函数执行进一步分析。
有关使用这些对象的详细信息，请参阅[Working with Probability Distributions](Working with Probability Distributions)。有关分布拟合器的编程工作流，请参阅 distributionFitter。

Distribution-Specific Functions and Generic Distribution Functions

Using distribution-specific functions and generic distribution functions is useful for:

• generating random numbers,
• computing summary statistics inside a loop or script, and passing a cdf or pdf as a function handle to another function.
• You can also use these functions to perform computations on arrays of parameter values rather than a single set of parameters.

Distribution-specific functions

— Some of the supported distributions have distribution-specific functions.
These functions use the following abbreviations, as in normpdf, normcdf, norminv, normstat, normfit, normlike, and normrnd:

• pdf — Probability density functions
• cdf — Cumulative distribution functions
• inv — Inverse cdf(cumulative distribution functions)
• stat — Distribution statistics functions
• fit — Distribution Fitter functions
• like — Negative loglikelihood functions
• rnd — Random number generators
  
  

Generic distribution functions

— Use cdf, icdf, mle, pdf, and random with a specified distribution name and parameters.

• cdf — Cumulative distribution function
• icdf — Inverse cdf(cumulative distribution function)
• mle — Distribution fitting function
• pdf — Probability density function
• random — Random number generating function

Analyze Distribution Using Distribution-Specific Functions

This example shows how to use distribution-specific functions to perform a multistep analysis on a fitted distribution.

The analysis illustrates how to:

• Fit a probability distribution to sample data that contains exam grades of 120 students by using normfit.
• Plot a histogram of the exam grade data, overlaid with a plot of the pdf of the fitted distribution, by using plot and normpdf.
• Compute the boundary for the top 10 percent of student grades by using norminv.
• Save the estimated distribution parameters by using save.

You can perform the same analysis using a probability distribution object.
See Analyze Distribution Using Probability Distribution Objects.