Defining an Estimation Engine

The estimation engine is the definition of the method through which model-fitting will take place, as defined by Bristlecone's EstimationEngine type. If you are competing multiple hypotheses, a single EstimationEngine will suffice for all model hypotheses.

Forthcoming

Time Modes

Bristlecone can run models in either discrete time, or continuous time. When running models in continuous time, an integration function is required:

Currently only fixed timesteps are supported, but variable timestep support (e.g. for sediment core data) is planned.

Two integration functions are included:

Solver	Function	Description
Runge-Kutta 4 (MathNet Numerics)	`Integration.MathNet.integrate`	A fourth-order Runge Kutta method to provide approximate solutions to ODE systems.
Runge-Kutta 547M (Open Solving Library for ODEs - Microsoft Research)	`Integration.MsftOslo.integrate`	A method based on classic Runge-Kutta, but with automatic error and step size control. See the documentation.

Optimisation

Bristlecone supports optimisation functions that have the following type signature:

type Optimise<'data> = int -> int -> Domain -> ('data[] -> 'data) -> ('data * 'data []) list

There are two optimsation techniques currently built-in:

Method	Function	Description
Amoeba (Nelder-Mead)	`Optimisation.Amoeba.solve`	A gradient-descent method.
MCMC Random Walk	`Optimisation.MonteCarlo.randomWalk`	A method based on classic Runge-Kutta, but with automatic error and step size control. See the documentation.

Estimation Engines

To use Bristlecone functions requires a configured EstimationEngine. The easiest way is with the helper functions within the Bristlecone module:

Function	Type	Description
`mkContinuous`	EstimationEngine	Default continuous engine
`mkDiscrete`	EstimationEngine	Default discrete model engine
`withContinuousTime`	t : Integrate<'a,'b> -> engine: EstimationEngine<'a,'b> -> EstimationEngine<'a,'b>	Transforms engine to continuous time mode, using the given integrator function.
`withConditioning`	c: Conditioning -> engine: EstimationEngine<'a,'b> -> EstimationEngine<'a,'b>	Choose how the start point is chosen when solving the model system.

type Optimise<'data> = int -> int -> obj -> ('data array -> 'data) -> ('data * 'data array) list

Multiple items
val int: value: 'T -> int (requires member op_Explicit)

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type int = int32

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type int<'Measure> = int

type 'T list = List<'T>