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 smoothed importance sampling 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 smoothed importance sampling 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 and Andrew Gelman (2015). Pareto smoothed importance sampling. arXiv preprint arXiv:1507.02646
• Aki Vehtari, Andrew Gelman and Jonah Gabry (2015). Efficient implementation of leave-one-out cross-validation and WAIC for evaluating fitted Bayesian models. 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