ContinuousUKFComponent

class probnum.filtsmooth.gaussian.approx.ContinuousUKFComponent(non_linear_model, spread=0.0001, priorpar=2.0, special_scale=0.0, mde_atol=1e-06, mde_rtol=1e-06, mde_solver='LSODA')

Bases: probnum.filtsmooth.gaussian.approx.UKFComponent, probnum.randprocs.markov.continuous.SDE

Continuous-time unscented Kalman filter transition.

Parameters
  • non_linear_model – Non-linear continuous-time model (SDE) that is approximated with the UKF.

  • mde_atol (Union[float, Real, floating, None]) – Absolute tolerance passed to the solver of the moment differential equations (MDEs). Optional. Default is 1e-6.

  • mde_rtol (Union[float, Real, floating, None]) – Relative tolerance passed to the solver of the moment differential equations (MDEs). Optional. Default is 1e-6.

  • mde_solver (Optional[str]) – Method that is chosen in scipy.integrate.solve_ivp. Any string that is compatible with solve_ivp(..., method=mde_solve,...) works here. Usual candidates are [RK45, LSODA, Radau, BDF, RK23, DOP853]. Optional. Default is LSODA.

Attributes Summary

state_dimension

wiener_process_dimension

Methods Summary

assemble_sigma_points(at_this_rv)

Assemble the sigma-points.

backward_realization(realization_obtained, rv)

Backward-pass of a realisation of a state, according to the transition.

backward_rv(rv_obtained, rv[, rv_forwarded, …])

Backward-pass of a state, according to the transition.

dispersion_function(t, x)

drift_function(t, x)

drift_jacobian(t, x)

forward_realization(realization, t[, dt, …])

Forward-pass of a realization of a state, according to the transition.

forward_rv(rv, t[, dt, compute_gain, …])

Forward-pass of a state, according to the transition.

jointly_transform_base_measure_realization_list_backward(…)

Transform samples from a base measure into joint backward samples from a list of random variables.

jointly_transform_base_measure_realization_list_forward(…)

Transform samples from a base measure into joint backward samples from a list of random variables.

smooth_list(rv_list, locations, _diffusion_list)

Apply smoothing to a list of random variables, according to the present transition.

Attributes Documentation

state_dimension
wiener_process_dimension

Methods Documentation

assemble_sigma_points(at_this_rv)

Assemble the sigma-points.

Return type

ndarray

backward_realization(realization_obtained, rv, rv_forwarded=None, gain=None, t=None, dt=None, _diffusion=1.0, _linearise_at=None)[source]

Backward-pass of a realisation of a state, according to the transition. In other words, return a description of

\[p(x(t) \,|\, {\mathcal{G}_t(x(t)) = \xi})\]

for an observed realization \(\xi\) of \({\mathcal{G}_t}(x(t))\). For example, this function is called in a Kalman update step.

Parameters
  • realization_obtained – Observed realization \(\xi\) as an array.

  • rv – “Current” distribution \(p(x(t))\) as a RandomVariable.

  • rv_forwarded – “Forwarded” distribution (think: \(p(\mathcal{G}_t(x(t)) \,|\, x(t))\)) as a RandomVariable. Optional. If provided (in conjunction with gain), computation might be more efficient, because most backward passes require the solution of a forward pass. If rv_forwarded is not provided, forward_rv() might be called internally (depending on the object) which is skipped if rv_forwarded has been provided

  • gain – Expected gain. Optional. If provided (in conjunction with rv_forwarded), some additional computations may be avoided (depending on the object).

  • t – Current time point.

  • dt – Increment \(\Delta t\). Ignored for discrete-time transitions.

  • _diffusion – Special diffusion of the driving stochastic process, which is used internally.

  • _linearise_at – Specific point of linearisation for approximate forward passes (think: extended Kalman filtering). Used internally for iterated filtering and smoothing.

Returns

  • RandomVariable – New state, after applying the backward-pass.

  • Dict – Information about the backward-pass.

backward_rv(rv_obtained, rv, rv_forwarded=None, gain=None, t=None, dt=None, _diffusion=1.0, _linearise_at=None)[source]

Backward-pass of a state, according to the transition. In other words, return a description of

\[p(x(t) \,|\, z_{\mathcal{G}_t}) = \int p(x(t) \,|\, z_{\mathcal{G}_t}, \mathcal{G}_t(x(t))) p(\mathcal{G}_t(x(t)) \,|\, z_{\mathcal{G}_t})) d \mathcal{G}_t(x(t)),\]

for observations \(z_{\mathcal{G}_t}\) of \({\mathcal{G}_t}(x(t))\). For example, this function is called in a Rauch-Tung-Striebel smoothing step, which computes a Gaussian distribution

\[p(x(t) \,|\, z_{\leq t+\Delta t}) = \int p(x(t) \,|\, z_{\leq t+\Delta t}, x(t+\Delta t)) p(x(t+\Delta t) \,|\, z_{\leq t+\Delta t})) d x(t+\Delta t),\]

from filtering distribution \(p(x(t) \,|\, z_{\leq t})\) and smoothing distribution \(p(x(t+\Delta t) \,|\, z_{\leq t+\Delta t})\), where \(z_{\leq t + \Delta t}\) contains both \(z_{\leq t}\) and \(z_{t + \Delta t}\).

Parameters
  • rv_obtained – “Incoming” distribution (think: \(p(x(t+\Delta t) \,|\, z_{\leq t+\Delta t})\)) as a RandomVariable.

  • rv – “Current” distribution (think: \(p(x(t) \,|\, z_{\leq t})\)) as a RandomVariable.

  • rv_forwarded – “Forwarded” distribution (think: \(p(x(t+\Delta t) \,|\, z_{\leq t})\)) as a RandomVariable. Optional. If provided (in conjunction with gain), computation might be more efficient, because most backward passes require the solution of a forward pass. If rv_forwarded is not provided, forward_rv() might be called internally (depending on the object) which is skipped if rv_forwarded has been provided

  • gain – Expected gain from “observing states at time \(t+\Delta t\) from time \(t\)). Optional. If provided (in conjunction with rv_forwarded), some additional computations may be avoided (depending on the object).

  • t – Current time point.

  • dt – Increment \(\Delta t\). Ignored for discrete-time transitions.

  • _diffusion – Special diffusion of the driving stochastic process, which is used internally.

  • _linearise_at – Specific point of linearisation for approximate forward passes (think: extended Kalman filtering). Used internally for iterated filtering and smoothing.

Returns

  • RandomVariable – New state, after applying the backward-pass.

  • Dict – Information about the backward-pass.

dispersion_function(t, x)
drift_function(t, x)
drift_jacobian(t, x)
forward_realization(realization, t, dt=None, compute_gain=False, _diffusion=1.0, _linearise_at=None)[source]

Forward-pass of a realization of a state, according to the transition. In other words, return a description of

\[p(\mathcal{G}_t[x(t)] \,|\, x(t)=\xi),\]

for some realization \(\xi\).

Parameters
  • realization – Realization \(\xi\) of the random variable \(x(t)\) that describes the current state.

  • t – Current time point.

  • dt – Increment \(\Delta t\). Ignored for discrete-time transitions.

  • compute_gain – Flag that indicates whether the expected gain of the forward transition shall be computed. This is important if the forward-pass is computed as part of a forward-backward pass, as it is for instance the case in a Kalman update.

  • _diffusion – Special diffusion of the driving stochastic process, which is used internally.

  • _linearise_at – Specific point of linearisation for approximate forward passes (think: extended Kalman filtering). Used internally for iterated filtering and smoothing.

Return type

Tuple[Normal, Dict]

Returns

  • RandomVariable – New state, after applying the forward-pass.

  • Dict – Information about the forward pass. Can for instance contain a gain key, if compute_gain was set to True (and if the transition supports this functionality).

forward_rv(rv, t, dt=None, compute_gain=False, _diffusion=1.0, _linearise_at=None)[source]

Forward-pass of a state, according to the transition. In other words, return a description of

\[p(\mathcal{G}_t[x(t)] \,|\, x(t)),\]

or, if we take a message passing perspective,

\[p(\mathcal{G}_t[x(t)] \,|\, x(t), z_{\leq t}),\]

for past observations \(z_{\leq t}\). (This perspective will be more interesting in light of backward_rv()).

Parameters
  • rv – Random variable that describes the current state.

  • t – Current time point.

  • dt – Increment \(\Delta t\). Ignored for discrete-time transitions.

  • compute_gain – Flag that indicates whether the expected gain of the forward transition shall be computed. This is important if the forward-pass is computed as part of a forward-backward pass, as it is for instance the case in a Kalman update.

  • _diffusion – Special diffusion of the driving stochastic process, which is used internally.

  • _linearise_at – Specific point of linearisation for approximate forward passes (think: extended Kalman filtering). Used internally for iterated filtering and smoothing.

Return type

Tuple[Normal, Dict]

Returns

  • RandomVariable – New state, after applying the forward-pass.

  • Dict – Information about the forward pass. Can for instance contain a gain key, if compute_gain was set to True (and if the transition supports this functionality).

jointly_transform_base_measure_realization_list_backward(base_measure_realizations, t, rv_list, _diffusion_list, _previous_posterior=None)

Transform samples from a base measure into joint backward samples from a list of random variables.

Parameters
  • base_measure_realizations (ndarray) – Base measure realizations (usually samples from a standard Normal distribution). These are transformed into joint realizations of the random variable list.

  • rv_list (_RandomVariableList) – List of random variables to be jointly sampled from.

  • t (Union[float, Real, floating]) – Locations of the random variables in the list. Assumed to be sorted.

  • _diffusion_list (ndarray) – List of diffusions that correspond to the intervals in the locations. If locations=(t0, …, tN), then _diffusion_list=(d1, …, dN), i.e. it contains one element less.

  • _previous_posterior – Previous posterior. Used for iterative posterior linearisation.

Returns

Jointly transformed realizations.

Return type

np.ndarray

jointly_transform_base_measure_realization_list_forward(base_measure_realizations, t, initrv, _diffusion_list, _previous_posterior=None)

Transform samples from a base measure into joint backward samples from a list of random variables.

Parameters
  • base_measure_realizations (ndarray) – Base measure realizations (usually samples from a standard Normal distribution). These are transformed into joint realizations of the random variable list.

  • initrv (RandomVariable) – Initial random variable.

  • t (Union[float, Real, floating]) – Locations of the random variables in the list. Assumed to be sorted.

  • _diffusion_list (ndarray) – List of diffusions that correspond to the intervals in the locations. If locations=(t0, …, tN), then _diffusion_list=(d1, …, dN), i.e. it contains one element less.

  • _previous_posterior – Previous posterior. Used for iterative posterior linearisation.

Returns

Jointly transformed realizations.

Return type

np.ndarray

smooth_list(rv_list, locations, _diffusion_list, _previous_posterior=None)

Apply smoothing to a list of random variables, according to the present transition.

Parameters
  • rv_list (_randomvariablelist._RandomVariableList) – List of random variables to be smoothed.

  • locations – Locations \(t\) of the random variables in the time-domain. Used for continuous-time transitions.

  • _diffusion_list – List of diffusions that correspond to the intervals in the locations. If locations=(t0, …, tN), then _diffusion_list=(d1, …, dN), i.e. it contains one element less.

  • _previous_posterior – Specify a previous posterior to improve linearisation in approximate backward passes. Used in iterated smoothing based on posterior linearisation.

Returns

List of smoothed random variables.

Return type

_randomvariablelist._RandomVariableList