estimation_algorithms

pomp currently implements the following algorithms for estimating model parameters:<ul>
<li><a rd-options="=mif2" href="/link/iterated%20filtering%20(IF2)?package=pomp&version=3.5&to=%3Dmif2" data-mini-rdoc="=mif2::iterated filtering (IF2)">iterated filtering (IF2)</a></li>
<li><a rd-options="=pmcmc" href="/link/particle%20Markov%20chain%20Monte%20Carlo%20(PMCMC)?package=pomp&version=3.5&to=%3Dpmcmc" data-mini-rdoc="=pmcmc::particle Markov chain Monte Carlo (PMCMC)">particle Markov chain Monte Carlo (PMCMC)</a></li>
<li><a rd-options="=abc" href="/link/approximate%20Bayesian%20computation%20(ABC)?package=pomp&version=3.5&to=%3Dabc" data-mini-rdoc="=abc::approximate Bayesian computation (ABC)">approximate Bayesian computation (ABC)</a></li>
<li><a rd-options="=probe_matching" href="/link/probe-matching%20via%20synthetic%20likelihood?package=pomp&version=3.5&to=%3Dprobe_matching" data-mini-rdoc="=probe_matching::probe-matching via synthetic likelihood">probe-matching via synthetic likelihood</a></li>
<li><a rd-options="=nonlinear_forecasting" href="/link/nonlinear%20forecasting?package=pomp&version=3.5&to=%3Dnonlinear_forecasting" data-mini-rdoc="=nonlinear_forecasting::nonlinear forecasting">nonlinear forecasting</a></li>
<li><a rd-options="=spectrum_matching" href="/link/power-spectrum%20matching?package=pomp&version=3.5&to=%3Dspectrum_matching" data-mini-rdoc="=spectrum_matching::power-spectrum matching">power-spectrum matching</a></li>
<li><a rd-options="=bsmc2" href="/link/Liu-West%20Bayesian%20sequential%20Monte%20Carlo?package=pomp&version=3.5&to=%3Dbsmc2" data-mini-rdoc="=bsmc2::Liu-West Bayesian sequential Monte Carlo">Liu-West Bayesian sequential Monte Carlo</a></li>
<li><a rd-options="=kalman" href="/link/Ensemble%20and%20ensemble-adjusted%20Kalman%20filters?package=pomp&version=3.5&to=%3Dkalman" data-mini-rdoc="=kalman::Ensemble and ensemble-adjusted Kalman filters">Ensemble and ensemble-adjusted Kalman filters</a></li>
</ul>

pomp

estimation_algorithms function

pomp currently implements the following algorithms for estimating model parameters:<ul>
<li><a rd-options='=mif2' href='iterated filtering (IF2)'>iterated filtering (IF2)</a></li>
<li><a rd-options='=pmcmc' href='particle Markov chain Monte Carlo (PMCMC)'>particle Markov chain Monte Carlo (PMCMC)</a></li>
<li><a rd-options='=abc' href='approximate Bayesian computation (ABC)'>approximate Bayesian computation (ABC)</a></li>
<li><a rd-options='=probe_matching' href='probe-matching via synthetic likelihood'>probe-matching via synthetic likelihood</a></li>
<li><a rd-options='=nonlinear_forecasting' href='nonlinear forecasting'>nonlinear forecasting</a></li>
<li><a rd-options='=spectrum_matching' href='power-spectrum matching'>power-spectrum matching</a></li>
<li><a rd-options='=bsmc2' href='Liu-West Bayesian sequential Monte Carlo'>Liu-West Bayesian sequential Monte Carlo</a></li>
<li><a rd-options='=kalman' href='Ensemble and ensemble-adjusted Kalman filters'>Ensemble and ensemble-adjusted Kalman filters</a></li>
</ul>

estimation_algorithms: Estimation algorithms for POMP models.

Description

Arguments

Details

See Also