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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
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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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Click on the caption to wacth the video
An introduction to the random walk Metropolis algorithm
Video of An introduction to the Random Walk Metropolis algorithm
The importance of step size for RWM (2D example)
Video of The importance of step size for Random Walk Metropolis
Constrained parameters? Use Metropolis-Hastings
Video of Constrained parameters? Use Metropolis-Hastings
Bob’s bees: the importance of using multiple bees (chains) to judge MCMC convergence
Video of Bob’s bees: the importance of using multiple bees (chains) to judge MCMC convergence