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Bayesian analysis is sometimes maligned by data analysts, due in part to the perceived element of arbitrariness associated with the selection of a meaningful prior distribution for a specific problem and the (former) difficulties involved with producing posterior distributions for all but the simplest situations.

On the other hand, we have heard it said that “while classical data analysts need a large bag of clever tricks to unleash on their data, Bayesians only ever really need one.” With the advent of efficient numerical samplers, modern data analysts cannot shy away from adding the Bayesian arrow to their quiver.

In this short report, we will introduce the basic concepts underpinning Bayesian analysis, and we present a small number of examples that illustrate the strengths of the approach.

Data Science Report Series #6: A Soft Introduction to Bayesian Data Analysis, by Ehssan Ghashim and Patrick Boily.

Other files and datasets of interests:
Dollar Bills R Notebook
Bayesian Data Analysis Slide Deck

Post Author: Patrick Boily

Patrick is a graduate from the University of Ottawa. He obtained his Ph.D. in Mathematics in 2006. He has taught over 35 courses at Universities in the Ottawa area since 1999, and worked on a number of projects as a federal public servant from 2008 to 2012. He joined Carleton University in 2012 to start and manage the Centre for Quantitative Analysis and Decision Support (CQADS), and is an Adjunct Professor at both the University of Ottawa and Carleton University. He is the president of Idlewyld Analytics and Consulting Services since 2016.

Patrick’s academic interests reside in the application of mathematics and statistics to evidence-based decision support. He has provided consulting services to numerous entities over the years, including United Way, the Public Health Agency of Canada, the Canadian Air Transport Security Authority, the Royal Canadian Mounted Police, Transport Canada, the Nuclear Waste Management Organization, the Privy Council Office, and Correctional Services Canada.

He has extensive experience in operations research, data science and predictive analytics, stochastic modeling, and simulations – managing and being involved in numerous projects in these subject areas from inception to completion. He also leads various workshops and training courses on data science and statistical analysis through the Data Action Lab.