LV_example_dataset

Data from a Lotka-Volterra ODE system with 2 species and 4 parameters.
Species in order are:
1. Sheep (Prey)
2. Wolves (Predators)

datasets

Efficient Bayesian parameter inference for systems of ordinary
differential equations. The inference is based on adaptive gradient matching
(AGM, Dondelinger et al. 2013 <http://proceedings.mlr.press/v31/dondelinger13a.pdf>,
Macdonald 2017 <http://theses.gla.ac.uk/7987/1/2017macdonaldphd.pdf>),
which offers orders-of-magnitude improvements in computational
efficiency over standard methods that require solving the differential
equation system. Features of the package include flexible specification
of custom ODE systems as R functions, support for missing variables,
Bayesian inference via population MCMC.

Frank Dondelinger

deGradInfer

Parameter Inference for Systems of Differential Equation

Benn Macdonald

LV_example_dataset function

A list with 5 components<dl class="dl-horizontal">
 <dt>data</dt><dd>Observed species with observation noise.</dd>
 <dt>data.true</dt><dd>Observed species without observation noise.</dd>
 <dt>time</dt><dd>Observed time points.</dd>
 <dt>params</dt><dd>Observed parameters.</dd>
 <dt>noise</dt><dd>Variance for the Gaussian observation noise.</dd>
</dl>

Format

Data from a Lotka-Volterra ODE system with 2 species and 4 parameters.
Species in order are:
1. Sheep (Prey)
2. Wolves (Predators) — LV_example_dataset

A list with 5 components<dl class='dl-horizontal'>
 <dt>data</dt><dd>Observed species with observation noise.</dd>
 <dt>data.true</dt><dd>Observed species without observation noise.</dd>
 <dt>time</dt><dd>Observed time points.</dd>
 <dt>params</dt><dd>Observed parameters.</dd>
 <dt>noise</dt><dd>Variance for the Gaussian observation noise.</dd>
</dl>

LV_example_dataset: Data from a Lotka-Volterra ODE system with 2 species and 4 parameters. Species in order are: 1. Sheep (Prey) 2. Wolves (Predators)

Description

Usage

Arguments

Format