Gaussian Distribution

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Gaussian Distribution
  • Gaussian/Normal Distribution
  • sigma = one standard deviation
  • Gaussian = Range of Doubtness
  • probability x is depend on the mew and sigma parameters
  • Fixed formula and no need to be remembered
  • Less the sigma, higher the probability, make a narrower and higher graph
  • more sigma, more doubtness, fatter gaussian distribution
  • we want to plot the Gaussian distribution of every examples
  • The problem is we don't know what to set mew and sigma squared parameter
  • Or maybe we can set the parameters?
  • We can set the parameters based on the distance of the parameters gathered
  • mew = average of the example
  • sigma squared = is the range of error. how much the variance of x from the average(mew) value. Summation of range error between x and amew
  • Maximum Likelihood Estimate is the name of formula above
  • ML tend to use m training set rather than m-1


  • Given a training set, now we can set mew and sigma for Gaussian Distribution
  • Next how to implement GD in Anomaly Detection