Ensemble algorithms

EnIS

Basic ensemble importance sampling (EnIS) inference algorithm. Also sometimes referred to as the "particle batch smoother" (PBS) or "generalized likelihood uncertainty estimation" (GLUE) depending on the context.

EKS <: EnsembleInferenceAlgorithm

Represents a proxy for the Ensemble Kalman Sampler (Garbuno-Inigo et al. 2020) implementation provided by EnsembleKalmanProcesses.

ESMDA <: EnsembleInferenceAlgorithm

Implementation of the ensemble-smother multiple data assimilation algorithm of Emerick et al. 2013.

Emerick, Alexandre A., and Albert C. Reynolds. "Ensemble smoother with multiple data assimilation." Computers & Geosciences 55 (2013): 3-15.

get_ensemble(sol::SimulatorInferenceSolution{<:EnsembleInferenceAlgorithm}, iter::Int=-1)

Fetches the state of the ensemble from the given solution object. For iterative algorithms, the optinal argument iter may be provided, which then retrieves the ensemble at the given iteration.

get_transformed_ensemble(sol::SimulatorInferenceSolution{<:EnsembleInferenceAlgorithm}, iter::Int=-1)

Fetches the transformed ensemble from the given solution object. For iterative algorithms, the optinal argument iter may be provided, which then retrieves the ensemble at the given iteration.

get_observables(sol::SimulatorInferenceSolution{<:EnsembleInferenceAlgorithm}, iter::Int=-1)

Returns a NamedTuple of concatenated observables at iteration iter.

ensemble_solve(
    ens::AbstractMatrix,
    initial_prob::SciMLBase.AbstractSciMLProblem,
    ensalg::SciMLBase.BasicEnsembleAlgorithm,
    dealg::Union{Nothing,SciMLBase.AbstractSciMLAlgorithm},
    param_map;
    iter::Integer=1,
    prob_func,
    output_func,
    pred_func,
    solve_kwargs...
)

Performs a single step/iteration for the given ensemble and returns a named tuple (; pred, sol) where sol are the full ensemble forward solutions and pred is the prediction matrix produced by pred_func.