Network Engineer
Clustering is a way to make sense of the data by grouping similar values into a group. There are many ways to achieve that and in this post we will be looking at one of the way based on spectral method. Spectral clustering provides a starting point to understand graphs with many nodes by clustering them into 2 or more clusters. This clustering technique can also be applied for analyzing general data. This technique is based on Linear algebra and Graph theory.
Following up from our previous post on Bayesian Finite Mixture Models, here are my notes on Non-Finite mixture model.
I have been lately looking at Bayesian Modelling which allows me to approach modelling problems from another perspective, especially when it comes to building Hierarchical Models. I think it will also be useful to approach a problem both via Frequentist and Bayesian to see how the models perform. Notes are from Bayesian Analysis with Python which I highly recommend as a starting book for learning applied Bayesian.
I recently observed a conversation around ECMP/Hash buckets which made me realize on how the end to end concept is not very well understood. So this provided me enough motivation to write about this topic which will be covered in various upcoming blog posts. But while thinking about the subject, I ran into an interesting RFC RFC2992. This RFC goes through a simple mathematical proof which I found impressive due to the fact that someone wrote that in ASCII in 2000. My intent in this blog post is to provide some colorization to the RFC and perhaps cover a bit more in detail.
A few years back my colleague introduced me to ECDF’s while doing some Time Series analysis. I quickly realized there elegance. This post is my attempt to pay it forward and would consider this post a success if at a bare minimum you are able to read an ECDF plot by the end of this post.