These files implement Pareto smoothed importance sampling (PSIS) and PSIS leave-one-out cross-validation for Matlab/Octave and Python (Python port made by Tuomas Sivula).
The corresponding R code can be found in R package called `loo' which is also available in CRAN.
- 'psislw.m' - Pareto smoothing of the log importance weights
- 'psisloo.m' - Pareto smoothed importance sampling leave-one-out log predictive densities
- 'gpdfitnew.m' - Estimate the paramaters for the Generalized Pareto Distribution
- 'sumlogs.m' - Sum of vector where numbers are represented by their logarithms
- 'psis.py' - Includes the following functions in a Python (Numpy) module
- psislw - Pareto smoothing of the log importance weights
- psisloo - Pareto smoothed importance sampling leave-one-out log predictive densities
- gpdfitnew - Estimate the paramaters for the Generalized Pareto Distribution
- gpinv - Inverse Generalised Pareto distribution function.
- sumlogs - Sum of vector where numbers are represented by their logarithms
- Aki Vehtari, Andrew Gelman and Jonah Gabry (2016). Pareto smoothed importance sampling. arXiv preprint arXiv:1507.02646
- Aki Vehtari, Andrew Gelman and Jonah Gabry (2016). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. In Statistics and Computing, doi:10.1007/s11222-016-9696-4. arXiv preprint arXiv:1507.04544
- Jin Zhang & Michael A. Stephens (2009) A New and Efficient Estimation Method for the Generalized Pareto Distribution, Technometrics, 51:3, 316-325, DOI: 10.1198/tech.2009.08017