Gaussian distribution (Normal distribution, 정규분포)

Seohyun·2023년 8월 8일
machine learning
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정규분포의 특징

  • a continuous probability distribution
  • if normal, mean (average) = median (midpoint) = mode (most frequent observation)
  • distribution falls symmetrically around the mean
  • width is defined by the standard deviation

정규분포식

f(x)=1σ2πe−12(x−μσ)2f(x)=\frac{1}{ \sigma \sqrt{2 \pi}} e ^{ - \frac{1}{2}( \frac{x- \mu}{\sigma})^2}
X∼N(μ, σ2),μ=0,σ=1X\sim N\left(\mu,\ \sigma^2\right), \mu = 0, \sigma = 1
  • x = value of the variable or data being examined and f(x) the probability function
  • μ = the mean
  • σ = the standard deviation
  • values of the mean (μ) and standard deviation (σ) are necessary
    • all normal distributions can be described by just two parameters: the mean and the standard deviation
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Seohyun
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이전 포스트

Volleyball dataset

다음 포스트

Convolutional/Max pooling layer의 output size 계산식

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