logistic_ode¶
- probnum.problems.zoo.filtsmooth.logistic_ode(y0=None, timespan=(0.0, 2.0), step=0.1, params=(6.0, 1.0), initrv=None, evlvar=None, ek0_or_ek1=1, order=3, forward_implementation='classic', backward_implementation='classic')[source]¶
Filtering/smoothing setup for a probabilistic ODE solver for the logistic ODE.
This state space model assumes an integrated Brownian motion prior on the dynamics and constructs the ODE likelihood based on the vector field defining the logistic ODE.
- Parameters
y0 (
Union
[Real
,ndarray
,None
]) – Initial conditions of the Initial Value Problemparams (
Tuple
[Real
,Real
]) – Parameters for the logistic ODEinitrv (
Optional
[RandomVariable
]) – Initial random variable of the probabilistic ODE solverevlvar (
Union
[Real
,ndarray
,None
]) – Seeprobnum.diffeq.GaussianIVPFilter
ek0_or_ek1 (
Integral
) – Seeprobnum.diffeq.GaussianIVPFilter
order (
Integral
) – Order of integration for the Integrated Brownian Motion prior of the solver.forward_implementation (
str
) – Implementation of the forward transitions inside prior and measurement model. Optional. Default is classic. For improved numerical stability, use sqrt.backward_implementation (
str
) – Implementation of the backward transitions inside prior and measurement model. Optional. Default is classic. For improved numerical stability, use sqrt.
- Returns
regression_problem –
TimeSeriesRegressionProblem
object with time points and zero-observations.info – Dictionary containing additional information like the prior process.
See also