Let’s learn about Goodman-Kruskal’s Lambda (GKL). This association measure is a Proportional Reduction in Error (PRE) measure. A PRE measure determines the reduction in the probability of error of one variable given another. GKL is an asymmetric measure; for two categorical variables, A and B, Lambda of A given B […]
Let’s learn new and modern ways of creating data containers in Python. Tuples? Try using named tuples. Dictionaries? Try using EasyDictionary. Classes? Try using data classes. These newer and alternative data containers help you write better and cleaner code. Let’s reach for success, let’s start learning.
Come learn about the basics of probabilistic and causal inference using Py-BBN. Py-BBN is an opensource API that anyone can use to solve and facilitate their decision making problems. This API is built using Python.
We are happy to release a statistical library for computing bivariate association called py-pair! There are over 130 association measures for all your data science needs! Install it with PyPi. Learn more with online documentation. See the source code on GitHub.
We are pleased to announce a turing_bbn, a C++17 exact inference API for causal and probabilistic reasoning. This product is a rewrite of the popular py-bbn API written in Python.
We are pleased to announce a first-of-a-kind product release for scalable learning of Bayesian Belief Networks using a cloud-first, cloud-native API! Please take a look at pyspark-bbn to learn more.
The Junction Tree Algorithm (JTA) is a popular, but quite sophisticated, algorithm used for exact inference in Bayesian Belief Network. Here are the conceptual steps visualized and explained.
Here’s a short post on probabilistic inference in Gaussian Bayesian Belief Networks. The math is elegant!
Let’s learn the basics of causal inference with Bayesian belief networks (BBNs) using Dr. Judea Pearl’s probability calculus. In particular, let’s see how the do-operator and backdoor criterion may help us estimate causal impact!
One-Off Coder is helping to fight against the spread of COVID-19 by exploring and experimenting with differential diagnosis models. In this post, we use Bayesian Belief Networks (BBN) to perform diagnostic reasoning from symptoms back to the cause. This approach can help patients be better triaged for screening and testing […]