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Mattias Villani
Professor of Statistics at Stockholm University
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Bayesian Learning
By
Mattias Villani
Edited
Bayesian Learning
Bernoulli distribution
Maximum likelihood - Bernoulli data
Bayes' theorem for events
Bayesian inference for Bernoulli iid data
Bayesian inference for Gaussian iid data with known variance
Maximum likelihood for iid Poisson data
Bayesian inference for iid Poisson counts
Bayesian inference for Exponential iid data
Bayesian credible intervals
Scaled inverse chi2 distribution
Bayesian inference for Gaussian iid data
Bayesian inference for multinomial data
Autoregressive processes - simulation and priors
Prior predictive - iid Poisson model with Gamma prior
Multivariate normal distribution
Bayesian linear regression
Central limit theorem
Law of Large Numbers
The Taylor approximation
Second derivative measures the curvature of a function
Random Walk Metropolis
Hamiltonian Markov Chain Monte Carlo
Leapfrog Integrator
HMC sampling from multi-modal distributions
Posterior approximation - Beta model for proportions
Dirichlet Distribution
Distributions on the unit simplex
Maximum likelihood - Poisson regression
Kullback-Leibler divergence - continuous distributions
Confidence interval for a proportion
Coverage of interval estimates for a proportion
Approximating the Binomial distribution
Sampling distribution and likelihood function - Normal model
Sampling distribution and likelihood function - Bernoulli model
Bayesian hypothesis test for a mean in a normal population
Distribution of the maximum - a tale of tails