means.approximation.lna package¶
Submodules¶
-
class
means.approximation.lna.lna.
LinearNoiseApproximation
(model)[source]¶ Bases:
means.approximation.approximation_baseclass.ApproximationBaseClass
A class to performs Linear Noise Approximation of a model.
Initialise the approximation.
Parameters: model ( Model
) – Model to approximate-
run
()[source]¶ Overrides the default _run() private method. Performs the complete analysis :return: A fully computed set of Ordinary Differential Equations that can be used for further simulation :rtype:
ODEProblem
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-
means.approximation.lna.lna.
lna_approximation
(model)[source]¶ A wrapper around
LinearNoiseApproximation
. It performs linear noise approximation (MEA).Returns: an ODE problem which can be further used in inference and simulation. Return type: ODEProblem
Module contents¶
Linear Noise Approximation¶
This part of the package implements Linear Noise Approximation as described in [Komorowski2009].
Example:
>>> from means.approximation.lna.lna import lna_approximation
>>> from means.examples.sample_models import MODEL_P53
>>> ode_problem = lna_approximation(MODEL_P53)
>>> print ode_problem
The result is an means.core.problems.ODEProblem
. Typically, it would be further used to
perform simulations (see simulation
) and inference (see inference
).
[Komorowski2009] |
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