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Bayesian
Allen Downey on Bayesian Methods, PyMC, and In-Demand Skills
Allen Downey, Professor of Computer Science at Olin College of Engineering, has built much of his career around Bayesian methods and the Python programming language. This in-demand skillset has been garnering increasing attention in the data science field, boosting its use in businesses and becoming more in-demand as a job... Read more
6 Applications of Bayesian Methods That You Should Know
As a data science professional, you likely hear the word “Bayesian” a lot. Whether it’s Bayesian Inference, Bayesian Statistics, or other Bayesian methods, knowing how to use and apply these methods is almost a necessity for any practicing professional. There are many different applications that one may use, and through... Read more
Introducing PyMC Labs: Saving the World with Bayesian Modeling
After I left Quantopian in 2020, something interesting happened: various companies contacted me inquiring about consulting to help them with their PyMC3 models. Usually, I don’t hear how people are using PyMC3 — they mostly show up on GitHub or Discourse when something isn’t working right. So, hearing about all these really cool projects was quite... Read more
The Bayesians are Coming! The Bayesians are Coming, to Time Series
Editor’s note: Aric is a speaker for ODSC West 2020 this October. Check out his talk, “The Bayesians are Coming! The Bayesians are Coming, to Time Series,” there!  Forecasting has applications across all industries. From needing to predict future values of sales for a product line, energy usage for a... Read more
How Bayesian Machine Learning Works
Bayesian methods assist several machine learning algorithms in extracting crucial information from small data sets and handling missing data. They play an important role in a vast range of areas from game development to drug discovery. Bayesian methods enable the estimation of uncertainty in predictions which proves vital for fields... Read more
Introduction to Bayesian Deep Learning
Bayes’ theorem is of fundamental importance to the field of data science, consisting of the disciplines: computer science, mathematical statistics, and probability. It is used to calculate the probability of an event occurring based on relevant existing information. Bayesian inference meanwhile leverages Bayes’ theorem to update the probability of a... Read more
From Idea to Insight: Using Bayesian Hierarchical Models to Predict Game Outcomes Part 2
What’s the best way to model the probability that one player beats another in a digital game a client of your employer designed? This is the second of a two-part series in which you’re a data scientist at a fictional mobile game development company that makes money by monetizing digital... Read more
Scikit Optimize: Bayesian Hyperparameter Optimization in Python
So you want to optimize hyperparameters of your machine learning model and you are thinking whether Scikit Optimize is the right tool for you? You are in the right place. In this article I will: show you an example of using skopt on a real problem, evaluate this library based on various... Read more
From Idea to Insight: Using Bayesian Hierarchical Models to Predict Game Outcomes Part 1
From Idea to Insight: Using Bayesian Hierarchical Models to Predict Game Outcomes Part 1. Imagine you’re a data scientist at an online mobile multiplayer competition platform. Your bosses have a vested interest in paying people with our skillset to predict game outcomes for a variety of commercial applications they profit... Read more
Hierarchical Bayesian Models in R
Hierarchical approaches to statistical modeling are integral to a data scientist’s skill set because hierarchical data is incredibly common. In this article, we’ll go through the advantages of employing hierarchical Bayesian models and go through an exercise building one in R. If you’re unfamiliar with Bayesian modeling, I recommend following... Read more