Learning how Gaussian distributions and the properties they have can help us perform anomaly detection on monitored data. In the first article of this series, we’ve discussed the properties of the Gaussian function and how they can be used to detect anomalies in monitored data. In this part, we will be putting that knowledge to practice and build our very own anomaly detection program.
Gaussian function and how they can be used to detect anomalies in monitored data. In this part, we will be putting that knowledge to practice and build our very own anomaly detection program.
To recap, we finished the previous article with the Gaussian function in the graph above, on which two points mark different values a given data sample x might take. We then stated that the farther away x is from the mean, the higher the probability it represents an anomaly.
Two points p(x1) and p(x2) plotted on a Gaussian function g(x) (image by author)
The only problem in that statement is that the probability of getting any single value is precisely zero. Why is that? It turns out that the Gaussian function is a member of a group of functions calls “Probability Density Functions”, or PDF’s for short. Without diving too deep into the mathematics behind PDFs, it suffices to understand that they give us the probability of a “continuous random variable” yielding a value within a given “range”.
Now let’s explain that: a range is simply a length between two numbers and is expressed by subtracting them. The common notation for the range between two points a and b is (a, b). A number x is considered “within” a range i
Become a data analysis expert using the R programming language in this [data science](https://360digitmg.com/usa/data-science-using-python-and-r-programming-in-dallas "data science") certification training in Dallas, TX. You will master data...
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
It generally covers statistics, mathematics, physics, economics, business, and management. Here, we’ll go to different reasons for those undergraduates to learn statistical programming.
Statistics for Data Science and Machine Learning Engineer. I’ll try to teach you just enough to be dangerous, and pique your interest just enough that you’ll go off and learn more.
🔵 Intellipaat Data Science with Python course: https://intellipaat.com/python-for-data-science-training/In this Data Science With Python Training video, you...