bayesRecon - Probabilistic Reconciliation via Conditioning
Provides methods for probabilistic reconciliation of hierarchical forecasts of time series. The available methods include analytical Gaussian reconciliation (Corani et al., 2021) <doi:10.1007/978-3-030-67664-3_13>, MCMC reconciliation of count time series (Corani et al., 2024) <doi:10.1016/j.ijforecast.2023.04.003>, Bottom-Up Importance Sampling (Zambon et al., 2024) <doi:10.1007/s11222-023-10343-y>, methods for the reconciliation of mixed hierarchies (Mix-Cond and TD-cond) (Zambon et al., 2024. The 40th Conference on Uncertainty in Artificial Intelligence, accepted).
Last updated 12 days ago
reconciliationtimeseries
7.13 score 7 stars 40 scripts 599 downloadsanMC - Compute High Dimensional Orthant Probabilities
Computationally efficient method to estimate orthant probabilities of high-dimensional Gaussian vectors. Further implements a function to compute conservative estimates of excursion sets under Gaussian random field priors.
Last updated 1 years ago
estimationgaussianorthantprobabilityopenblascpp
3.88 score 5 dependents 6 scripts 445 downloadsKrigInv - Kriging-Based Inversion for Deterministic and Noisy Computer Experiments
Criteria and algorithms for sequentially estimating level sets of a multivariate numerical function, possibly observed with noise.
Last updated 2 years ago
2.81 score 4 dependents 54 scripts 547 downloadsprofExtrema - Compute and Visualize Profile Extrema Functions
Computes profile extrema functions for arbitrary functions. If the function is expensive-to-evaluate it computes profile extrema by emulating the function with a Gaussian process (using package 'DiceKriging'). In this case uncertainty quantification on the profile extrema can also be computed. The different plotting functions for profile extrema give the user a tool to better locate excursion sets.
Last updated 3 months ago
2.70 score 10 scripts 128 downloadspGPx - Pseudo-Realizations for Gaussian Process Excursions
Computes pseudo-realizations from the posterior distribution of a Gaussian Process (GP) with the method described in Azzimonti et al. (2016) <doi:10.1137/141000749>. The realizations are obtained from simulations of the field at few well chosen points that minimize the expected distance in measure between the true excursion set of the field and the approximate one. Also implements a R interface for (the main function of) Distance Transform of sampled Functions (<https://cs.brown.edu/people/pfelzens/dt/index.html>).
Last updated 1 years ago
cpp
2.18 score 1 dependents 9 scripts 153 downloads