I'm pleased to announce the py_emra package for Ensemble Modeling Robustness Analysis (EMRA). EMRA is a tool for modeling metabolic systems which I have worked on through my PhD. The crux of EMRA is to use dynamic stability as a criteria for model selection and simulation in metabolic systems (e.g. bacterial metabolism). This depends on using… Continue reading The py_emra package

# Month: July 2016

## Distributed Computing: Motivation & File Systems

Why Distributed Computing? Distributed computing is the practice of computing using a cluster of computers rather than a single one. Distributed computing comes with many advantages. Work With Very Big Data Work for today's data scientists very often involves data sets which are too large to feasibly work with from one local computer. For simple installations of programming… Continue reading Distributed Computing: Motivation & File Systems

## Some of my favorite links

This is a post which I will update periodically with some of my favorite Quora (& Stackexchange) answers. Gamma & Poisson Distributions Why is Spark faster than MapReduce? Importance of independence in statistical modeling Importance of underlying data distributions Biggest lessons learned in corporate Free open datasets Most common data science mistakes SVM and Kernels… Continue reading Some of my favorite links

## Bayesian Updating, Part 2

In Part 1, I explored how Bayesian updating operates when there are two discrete possibilities. I now investigate how Bayesian updating operates with one continuous parameter. This example is from Chapter 2 of 'Statistical Rethinking' by Richard McElreath. The premise can be paraphrased as follows: Suppose you have a globe representing the Earth. It is… Continue reading Bayesian Updating, Part 2