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Fundamentals of Bayesian Data Analysis in R

Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.

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Course Description

Bayesian data analysis is an approach to statistical modeling and machine learning that is becoming more and more popular. It provides a uniform framework to build problem specific models that can be used for both statistical inference and for prediction. This course will introduce you to Bayesian data analysis: What it is, how it works, and why it is a useful tool to have in your data science toolbox.
  1. 1

    What is Bayesian Data Analysis?

    Free

    This chapter will introduce you to Bayesian data analysis and give you a feel for how it works.

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    A first taste of Bayes
    50 xp
    Unknowns and ice creams
    50 xp
    Let's try some Bayesian data analysis
    50 xp
    Coin flips with prop_model
    100 xp
    Zombie drugs with prop_model
    100 xp
    Samples and posterior summaries
    50 xp
    Looking at samples from prop_model
    100 xp
    Summarizing the zombie drug experiment
    100 xp
    You've done some Bayesian data analysis!
    50 xp
  2. 2

    How does Bayesian inference work?

    In this chapter we will take a detailed look at the foundations of Bayesian inference.

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    The parts needed for Bayesian inference
    50 xp
    Take a generative model for a spin
    100 xp
    Take the binomial distribution for a spin
    100 xp
    Using a generative model
    50 xp
    How many visitors could your site get (1)?
    100 xp
    Representing uncertainty with priors
    50 xp
    Adding a prior to the model
    100 xp
    Bayesian models and conditioning
    50 xp
    Update a Bayesian model with data
    100 xp
    How many visitors could your site get (3)?
    100 xp
    What have we done?
    50 xp
  3. 3

    Why use Bayesian Data Analysis?

    This chapter will show you four reasons why Bayesian data analysis is a useful tool to have in your data science tool belt.

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    Four good things with Bayes
    50 xp
    Explore using the Beta distribution as a prior
    100 xp
    Pick the prior that best captures the information
    50 xp
    Change the model to use an informative prior
    100 xp
    Contrasts and comparisons
    50 xp
    Fit the model using another dataset
    100 xp
    Calculating the posterior difference
    100 xp
    Decision analysis
    50 xp
    A small decision analysis 1
    100 xp
    A small decision analysis 2
    100 xp
    Change anything and everything
    50 xp
    The Poisson distribution
    100 xp
    Clicks per day instead of clicks per ad
    100 xp
    Bayes is optimal, kind of...
    50 xp
  4. 4

    Bayesian inference with Bayes' theorem

    Learn what Bayes theorem is all about and how to use it for statistical inference.

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    Probability rules
    50 xp
    Cards and the sum rule
    100 xp
    Cards and the product rule
    100 xp
    Calculating likelihoods
    50 xp
    From rbinom to dbinom
    100 xp
    Calculating probabilities with dbinom
    100 xp
    Bayesian calculation
    50 xp
    Calculating a joint distribution
    100 xp
    Conditioning on the data (again)
    100 xp
    A conditional shortcut
    100 xp
    Bayes' theorem
    50 xp
    A Poisson model description
    50 xp
  5. 5

    More parameters, more data, and more Bayes

    Learn about using the Normal distribution to analyze continuous data and try out a tool for practical Bayesian analysis in R.

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    The temperature in a Normal lake
    50 xp
    rnorm, dnorm, and the weight of newborns
    100 xp
    A Bayesian model of water temperature
    50 xp
    A Bayesian model of Zombie IQ
    100 xp
    Eyeballing the mean IQ of zombies?
    50 xp
    Answering the question: Should I have a beach party?
    50 xp
    Sampling from the zombie posterior
    100 xp
    But how smart will the next zombie be?
    100 xp
    A practical tool: BEST
    50 xp
    The BEST models and zombies on a diet
    100 xp
    BEST is robust
    100 xp
    What have you learned? What did we miss?
    50 xp

In the following tracks

Machine Learning Scientist with RStatistician with R

Collaborators

Chester Ismay
Nick Solomon

Prerequisites

Introduction to R
Rasmus Bååth HeadshotRasmus Bååth

Data Science Lead at castle.io

Rasmus Bååth is a Data Science Lead at castle.io. Previously, he was an instructor and Curriculum Lead for Projects at DataCamp. He has a PhD in Cognitive Science from Lund University in Sweden. Follow him at @rabaath on Twitter or on his blog, Publishable Stuff.
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