Approximate leave-future-out cross-validation for Bayesian time series models

1 code implementation • 17 Feb 2019 • Paul-Christian Bürkner, Jonah Gabry, Aki Vehtari

One of the common goals of time series analysis is to use the observed series to inform predictions for future observations.

Methodology

Visualization in Bayesian workflow

2 code implementations • 5 Sep 2017 • Jonah Gabry, Daniel Simpson, Aki Vehtari, Michael Betancourt, Andrew Gelman

Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains.

Methodology Applications

Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC

7 code implementations • 16 Jul 2015 • Aki Vehtari, Andrew Gelman, Jonah Gabry

Leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model using the log-likelihood evaluated at the posterior simulations of the parameter values.

Computation Methodology

Pareto Smoothed Importance Sampling

9 code implementations • 9 Jul 2015 • Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry

Importance weighting is a general way to adjust Monte Carlo integration to account for draws from the wrong distribution, but the resulting estimate can be highly variable when the importance ratios have a heavy right tail.