DiscreteGaussianModel¶
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class
probnum.filtsmooth.
DiscreteGaussianModel
(dynafct, diffmatfct, jacfct=None)¶ Bases:
probnum.filtsmooth.DiscreteModel
Discrete Gaussian transition models of the form
\[x_{i+1} \sim \mathcal{N}(g(t_i, x_i), S(t_i))\]for some (potentially non-linear) dynamics \(g\) and diffusion matrix \(S\).
Parameters: - dynafct (callable) – Dynamics function \(g=g(t, x)\). Signature:
dynafct(t, x)
. - diffmatfct (callable) – Diffusion matrix function \(S=S(t)\). Signature:
diffmatfct(t)
. - jacfct (callable, optional.) – Jacobian of the dynamics function \(g\), \(Jg=Jg(t, x)\).
Signature:
jacfct(t, x)
.
See also
Attributes Summary
dimension
Dimension of the transition model. Methods Summary
__call__
(arr_or_rv, RandomVariable], start, …)Transition a random variable or a realization of one. diffusionmatrix
(time, **kwargs)Compute diffusion matrix \(S=S(t)\) at time \(t\). dynamics
(time, state, **kwargs)Compute dynamics \(g=g(t, x)\) at time \(t\) and state \(x\). jacobian
(time, state, **kwargs)Compute diffusion matrix \(S=S(t)\) at time \(t\). transition_realization
(real, start[, stop])Transition a realization of a random variable from time \(t\) to time \(t+\Delta t\). transition_rv
(rv, start[, stop])Transition a random variable from time \(t\) to time \(t+\Delta t\). Attributes Documentation
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dimension
¶ Dimension of the transition model.
Methods Documentation
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__call__
(arr_or_rv: Union[numpy.ndarray, RandomVariable], start: float = None, stop: float = None, **kwargs) -> ('RandomVariable', typing.Dict)¶ Transition a random variable or a realization of one.
The input is either interpreted as a random variable or as a realization. Accordingly, the respective methods are called:
transition_realization()
ortransition_rv()
.
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diffusionmatrix
(time, **kwargs)[source]¶ Compute diffusion matrix \(S=S(t)\) at time \(t\).
Parameters: time (float) – Time \(t\). Returns: Diffusion matrix \(S=S(t)\). Return type: np.ndarray
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dynamics
(time, state, **kwargs)[source]¶ Compute dynamics \(g=g(t, x)\) at time \(t\) and state \(x\).
Parameters: - time (float) – Time \(t\).
- state (array_like) – State \(x\). For instance, realization of a random variable.
Returns: Evaluation of \(g=g(t, x)\).
Return type: np.ndarray
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jacobian
(time, state, **kwargs)[source]¶ Compute diffusion matrix \(S=S(t)\) at time \(t\).
Parameters: - time (float) – Time \(t\).
- state (array_like) – State \(x\). For instance, realization of a random variable.
Raises: NotImplementedError
– If the Jacobian is not implemented. This is the case ifjacfct()
is not specified at initialization.Returns: Evaluation of the Jacobian \(J g=Jg(t, x)\).
Return type: np.ndarray
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transition_realization
(real, start, stop=None)[source]¶ Transition a realization of a random variable from time \(t\) to time \(t+\Delta t\).
For random variable \(x_t\), it returns the random variable defined by
\[x_{t + \Delta t} \sim p(x_{t + \Delta t} | x_t = r) .\]This is different to
transition_rv()
which computes the parametrization of \(x_{t + \Delta t}\) based on the parametrization of \(x_t\).Nb: Think of transition as a verb, i.e. this method “transitions” a realization of a random variable.
Parameters: - real – Realization of the random variable.
- start – Starting point \(t\).
- stop – End point \(t + \Delta t\).
Returns: - RandomVariable – Random variable, describing the state at time \(t + \Delta t\) based on realization at time \(t\).
- dict – Additional information in form of a dictionary, for instance the cross-covariance in the prediction step, access to which is useful in smoothing.
See also
transition_rv()
- Apply transition to a random variable.
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transition_rv
(rv, start, stop=None, **kwargs)[source]¶ Transition a random variable from time \(t\) to time \(t+\Delta t\).
For random variable \(x_t\), it returns the random variable defined by
\[x_{t + \Delta t} \sim p(x_{t + \Delta t} | x_t) .\]This returns a random variable where the parametrization depends on the paramtrization of \(x_t\). This is different to
transition_rv()
which computes the parametrization of \(x_{t + \Delta t}\) based on a realization of \(x_t\).Nb: Think of transition as a verb, i.e. this method “transitions” a random variable.
Parameters: - rv – Realization of the random variable.
- start – Starting point \(t\).
- stop – End point \(t + \Delta t\).
Returns: - RandomVariable – Random variable, describing the state at time \(t + \Delta t\) based on realization at time \(t\).
- dict – Additional information in form of a dictionary, for instance the cross-covariance in the prediction step, access to which is useful in smoothing.
See also
transition_realization()
- Apply transition to a realization of a random variable.
- dynafct (callable) – Dynamics function \(g=g(t, x)\). Signature: