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- Iterated_filtering abstract "Iterated filtering algorithms are a tool for maximum likelihood inference on partially observed dynamical systems. Stochastic perturbations to the unknown parameters are used to explore the parameter space. Applying sequential Monte Carlo (the particle filter) to this extended model results in the selection of the parameter values that are more consistent with the data. Appropriately constructed procedures, iterating with successively diminished perturbations, converge to the maximum likelihood estimate. Iterated filtering methods have so far been used most extensively to study the infectious disease transmission dynamics. Case studies include cholera, influenza, malaria, HIV, pertussis and measles. Other areas which have been proposed to be suitable for these methods include ecological dynamics and finance.The perturbations to the parameter space play several different roles. Firstly, they smooth out the likelihood surface, enabling the algorithm to overcome small-scale features of the likelihood during early stages of the global search. Secondly, Monte Carlo variation allows the search to escape from local minima. Thirdly, the iterated filtering update uses the perturbed parameter values to construct an approximation to the derivative of the log likelihood even though this quantity is not typically available in closed form. Fourthly, the parameter perturbations help to overcome numerical difficulties that can arise during sequential Monte Carlo.".
- Iterated_filtering wikiPageExternalLink pomp.r-forge.r-project.org.
- Iterated_filtering wikiPageID "31745436".
- Iterated_filtering wikiPageRevisionID "578155696".
- Iterated_filtering hasPhotoCollection Iterated_filtering.
- Iterated_filtering subject Category:Dynamical_systems.
- Iterated_filtering subject Category:Monte_Carlo_methods.
- Iterated_filtering subject Category:Nonlinear_filters.
- Iterated_filtering subject Category:Statistical_algorithms.
- Iterated_filtering type Ability105616246.
- Iterated_filtering type Abstraction100002137.
- Iterated_filtering type Act100030358.
- Iterated_filtering type Activity100407535.
- Iterated_filtering type Algorithm105847438.
- Iterated_filtering type Attribute100024264.
- Iterated_filtering type Cognition100023271.
- Iterated_filtering type DynamicalSystem106246361.
- Iterated_filtering type DynamicalSystems.
- Iterated_filtering type Event100029378.
- Iterated_filtering type Know-how105616786.
- Iterated_filtering type Method105660268.
- Iterated_filtering type MonteCarloMethods.
- Iterated_filtering type PhaseSpace100029114.
- Iterated_filtering type Procedure101023820.
- Iterated_filtering type PsychologicalFeature100023100.
- Iterated_filtering type Rule105846932.
- Iterated_filtering type Space100028651.
- Iterated_filtering type StatisticalAlgorithms.
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- Iterated_filtering comment "Iterated filtering algorithms are a tool for maximum likelihood inference on partially observed dynamical systems. Stochastic perturbations to the unknown parameters are used to explore the parameter space. Applying sequential Monte Carlo (the particle filter) to this extended model results in the selection of the parameter values that are more consistent with the data.".
- Iterated_filtering label "Iterated filtering".
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- Iterated_filtering sameAs Q17081875.
- Iterated_filtering sameAs Q17081875.
- Iterated_filtering sameAs Iterated_filtering.
- Iterated_filtering wasDerivedFrom Iterated_filtering?oldid=578155696.
- Iterated_filtering isPrimaryTopicOf Iterated_filtering.