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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... 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... Read more
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 from, for example, setting odds and sharing better insights with game developers on... 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... Read more