# What is the analytical formula for "Kaiming He" probability density function?

A probability density function is a real-valued function that roughly gives the density of probability at a particular value of a random variable.

For example, the probability density function of a normal random variable is given by

$$f(x) = \dfrac{1}{2\sigma \sqrt{2\pi}} e^{-{\LARGE(}\dfrac{x-\mu}{\sigma}{\LARGE)}^2}$$

Uniform Kaiming He probability distribution function is used for initialization of weights in Convolutional neural networks in PyTorch and the distribution function was initially mentioned in the research paper titled Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification by Kaiming He et al. I think.

What is the analytical formula for the Kaiming He probability density function?

• Uniform: $$\boldsymbol{\mathit{U}}(-\sqrt{6 / n_{j}}, +\sqrt{6 / n_{j}})$$
• Normal: $$\boldsymbol{\mathbf{\mathit{N}}}(0, \sqrt{2/n_{j}})$$