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  • The Algorithm on how to Implement Gaussian Distribution for Anomaly Detection

  • Now introduce a Density estimation, use for estimating where the p(x) on (epsilon) threshold value
  • the x itself is matrix of vector. where x(i) is a vector of features. And each of x has each mew and sigma value
  • funny symbol = product of each iteration
  • Dense way for writing formula

  • Choose which features that probably is the anomalous examples(too small or too big)
  • Possible to produce vectorized implementation for mew and sigma
  • According to step 3, compute p(x) and observe if it smaller than the threshold value
  • Set manually epsilon (discussed later how to automatically set)
  • p(x1) based on result is not an anomaly, whereas p(x2) prove x2 is an anomaly
  • all points outside the circle region is an anomaly
  • As said before inside the circle is sure, outside is doubtly(anomaly).

  • We now know how to  getting data set, taking mew and sigma into account and determining whether xi is the anomalous examples
  • How to actually get this to work well in Anomaly Detection