estimation_algorithms: Parameter estimation algorithms for POMP models.

In kingaa/pomp: Statistical Inference for Partially Observed Markov Processes

Parameter estimation algorithms for POMP models.

Description

pomp currently implements the following algorithms for estimating model parameters:

• iterated filtering (IF2)

• particle Markov chain Monte Carlo (PMCMC)

• approximate Bayesian computation (ABC)

• probe-matching via synthetic likelihood

• nonlinear forecasting

• power-spectrum matching

• Liu-West Bayesian sequential Monte Carlo

• Ensemble and ensemble-adjusted Kalman filters

Details

Help pages detailing each estimation algorithm are provided.

See Also

basic model components, workhorse functions, elementary algorithms.

More on pomp estimation algorithms: abc(), bsmc2(), mif2(), nlf, pmcmc(), pomp-package, probe_match, spect_match

kingaa/pomp documentation built on April 24, 2024, 11:25 a.m.