I refer to this site.
https://catindog.hatenablog.com/entry/2018/07/29/172210
In the above URL, the standardized rank is passed to the erfinv function within the range of (0.5, 0.5), and the returned value is shaped like a normal distribution.
Rank Gauss is a technique used to approximate a normal distribution to a biased graph
After ranking the magnitude of the continuous value, standardize the rank to fit within a specific range
After that, it seems to pass x to the inverse error function called erfinv
I don't know how the inverse error function here is getting closer to the normal distribution.
First, I looked at the error function. The error function seems to return the probability that the error will fall within a certain range when the normal distribution is assumed.
The erfinv function seems to be the inverse function.
The inverse function seems to hold the relationship erfinv (erf (x)) = x.
I'd be happy if you could just provide a hint or link to the page.

Answer # 1
Related articles
 what is the significance of the loss function in machine learning?
 machine learning  error when learning yolo format original data set in googlecolab/yolov3/darknet environment
 python  in machine learning learning, the accuracy is 100% and the value of the loss function is almost 0 from the beginning an
 machine learning  i don't understand the content of the error (keras, conv2d)
 machine learning  unicodedecodeerror error occurs when creating original model of kerasyolo3
 error when machine learning in python
 machine learning  learning mechanism with original data in yolov3 (keras)
 javascript  map is not a function error
 machine learning of time series data: when the correct label has multiple ratios
 python  pandas read_csv function error reading data_csv file (macos)
 machine learning  i want to know the processing contents for the input data at the time of inference of the batch normalization
 machine learning  structure of darknet53
 python  recognition of movements in machine learning
 php  error when implementing line login function by socialite
 python  about gpu of notebook pc to run machine learning system
 machine learning (python) is too accurate
 python  how to use npasarray in machine learning
 html  i am learning rspec, but the error is stagnant because i don't understand what it is
 machine learning  best practices for variable selection in classification problems
 array error in fortran function
 php  coincheck api authentication doesn't work
 php  i would like to introduce the coincheck api so that i can make payments with bitcoin on my ec site
 [php] i want to get account information using coincheck api
 python  you may need to restart the kernel to use updated packages error
 python 3x  typeerror: 'method' object is not subscriptable
 the emulator process for avd pixel_2_api_29 was killed occurred when the android studio emulator was started, so i would like to
 javascript  how to check if an element exists in puppeteer
 xcode  pod install [!] no `podfile 'found in the project directory
 vuejs  [vuetify] unable to locate target [dataapp] i want to unit test to avoid warning
 android studio  unresolved reference comes out in kotlin
Some comments may be inaccurate, but I will comment.
First, in order to understand the inverse error function, it is necessary to know the error function.
Roughly speaking, the error function is a function that returns the cumulative sum p of probability densities in a random variable x for a normal distribution. Strictly speaking, the cumulative sum of probability density is calculated by the cumulative distribution function, but since both are in a linear relationship, the error function seems to be a function that outputs the cumulative distribution.
Since the inverse error function is the inverse of the input and output, it is a function that returns a random variable x whose cumulative density is p for a normal distribution.
This inverse error function outputs a value with nonequal intervals according to a specified normal distribution when a value with 0 to 1 equally divided is input. By using this characteristic, it is possible to convert random values that follow a uniform distribution into random values that follow a normal distribution.