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A Student’s Guide to Bayesian Statistics
by
Ben Lambert
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Student Resources
How to best use this book
The subjective worlds of Frequentist and Bayesian statistics
Author videos
Answers to Problem Sets
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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Author videos
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What is a hierarchical model?
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Pooled versus heterogeneous coefficients versus hierarchical models?
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What are the benefits of hierarchical models?
Video coming soon!
What are non-centred parameterisations and why are they useful?
Video coming soon!
