How to best use this book

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The subjective worlds of Frequentist and Bayesian statistics

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Probability - the nuts and bolts of Bayesian inference

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Likelihoods

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Priors

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The devil is in the denominator

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The posterior - the goal of Bayesian inference

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An introduction to distributions for the mathematically uninclined

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Conjugate priors

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Evaluation of model fit and hypothesis testing

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Making Bayesian analysis objective?

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Leaving conjugates behind: Markov Chain Monte Carlo

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Random Walk Metropolis

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Gibbs sampling

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Hamiltonian Monte Carlo

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Stan

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Hierarchical models

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Linear regression models

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Generalised linear models and other animals

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