Autoregressive processes - simulation and priors / Mattias Villani | Observable

Mattias Villani

Professor of Statistics at Stockholm University

Public

Edited

Fork of Student-t distribution (three-parameter)

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2 forks

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

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