ConstantDiffusion

class probnum.randprocs.markov.continuous.ConstantDiffusion

Bases: Diffusion

Constant diffusion and its calibration.

Methods Summary

__call__(t)	Evaluate the diffusion \(\sigma(t)\) at \(t\).
estimate_locally(meas_rv, ...)	Estimate the (local) diffusion and update current (global) estimation in- place.
update_in_place(local_estimate, t)

Methods Documentation

__call__(t)

Evaluate the diffusion \(\sigma(t)\) at \(t\).

Parameters

t (Union[_SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) –

Return type

Union[_SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]], ndarray]

estimate_locally(meas_rv, meas_rv_assuming_zero_previous_cov, t)

Estimate the (local) diffusion and update current (global) estimation in- place.

Used for uncertainty calibration in the ODE solver.

Parameters

• meas_rv (RandomVariable) –

• meas_rv_assuming_zero_previous_cov (RandomVariable) –

• t (Union[float, Real, floating]) –

Return type

Union[_SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]

update_in_place(local_estimate, t)