bayesian Archives - Open Data Science - Your News Source for AI, Machine Learning & more

Allen Downey on Bayesian Methods, PyMC, and In-Demand Skills

Modeling AI bayesianposted by Alex Landa, ODSC August 9, 2021

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... Read more

Incremental Development of PyMC Models

Modeling Statistics AI bayesian PyMCposted by ODSC Community July 28, 2021

PyMC is a powerful tool for doing Bayesian statistics, but getting started can be intimidating. This article presents an example that I think is a good starting place, and demonstrates a method I use to develop and test models incrementally. Games like hockey and soccer are... Read more

The Bayesians are Coming! The Bayesians are Coming, to Time Series

Modeling Statistics bayesian West 2020posted by ODSC Community September 28, 2020

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,... Read more

How Bayesian Machine Learning Works

Machine Learning Modeling bayesianposted by ODSC Community September 22, 2020

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... Read more

Introduction to Bayesian Deep Learning

Deep Learning Modeling bayesianposted by Daniel Gutierrez, ODSC July 21, 2020

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... Read more

From Idea to Insight: Using Bayesian Hierarchical Models to Predict Game Outcomes Part 2

Modeling Statistics bayesian Bayesian Hierarchical Modelsposted by Brandon Dey, ODSC January 30, 2020

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... Read more

From Idea to Insight: Using Bayesian Hierarchical Models to Predict Game Outcomes Part 1

Modeling Statistics bayesian Hierarchical Bayesian Modelsposted by Brandon Dey, ODSC January 17, 2020

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... Read more

Hierarchical Bayesian Models in R

Modeling R Statistics Tools & Languages bayesian hierarchical bayesian Hierarchical Bayesian Models Statisticsposted by Nathaniel Jermain August 19, 2019

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... Read more

Building Your First Bayesian Model in R

Machine Learning Modeling bayesianposted by Nathaniel Jermain June 5, 2019

Bayesian models offer a method for making probabilistic predictions about the state of the world. Key advantages over a frequentist framework include the ability to incorporate prior information into the analysis, estimate missing values along with parameter values, and make statements about the probability of a... Read more