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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 interested in the applications of mathematics and statistcs to evidence-based decision support. He has worked on 25+ such projects since 2008. He has extensive experience in data science, machine learning, A.I. and predictive analytics, data cleaning and data visualization.