Algorithm

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