Matern

class probnum.diffeq.Matern(ordint, spatialdim, lengthscale, diffconst, precond_step=1.0)[source]

Bases: probnum.diffeq.odefiltsmooth.prior.ODEPrior

Matern(q) prior –> Matern process with reg. q+0.5 and hence, with matrix size q+1

F = I_d otimes F L = I_d otimes L = I_d otimes diffconst*(0, …, 1) Q = I_d

Attributes Summary

diffusionmatrix Evaluates Q.
dispersionmatrix
driftmatrix
force
inverse_preconditioner Convenience property to return the readily-computed inverse preconditioner without having to remember abbreviations.
ndim Spatial dimension (utility attribute).
preconditioner Convenience property to return the readily-computed preconditioner without having to remember abbreviations.

Methods Summary

chapmankolmogorov(start, stop, step, …) Solves Chapman-Kolmogorov equation from start to stop via step.
dispersion(time, state, **kwargs) Evaluates l(t, x(t)) = L(t).
drift(time, state, **kwargs) Evaluates f(t, x(t)) = F(t) x(t) + u(t).
jacobian(time, state, **kwargs) maps t -> F(t)
precond2nordsieck(step) Computes preconditioner inspired by Nordsieck.
proj2coord(coord) Projection matrix to \(i\)-th coordinates.
sample(start, stop, step, initstate, **kwargs) Samples from initstate at start to stop with stepsize step.

Attributes Documentation

diffusionmatrix

Evaluates Q.

dispersionmatrix
driftmatrix
force
inverse_preconditioner

Convenience property to return the readily-computed inverse preconditioner without having to remember abbreviations.

Returns:Inverse preconditioner matrix \(P^{-1}\)
Return type:np.ndarray, shape=(d(q+1), d(q+1))
ndim

Spatial dimension (utility attribute).

preconditioner

Convenience property to return the readily-computed preconditioner without having to remember abbreviations.

Returns:Preconditioner matrix \(P\)
Return type:np.ndarray, shape=(d(q+1), d(q+1))

Methods Documentation

chapmankolmogorov(start, stop, step, randvar, **kwargs)

Solves Chapman-Kolmogorov equation from start to stop via step.

For LTISDEs, there is a closed form solutions to the ODE for mean and kernels (see super().chapmankolmogorov(…)). We exploit this for [(stop - start)/step] steps.

References

Eq. (8) in http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.390.380&rep=rep1&type=pdf and Eq. 6.41 and Eq. 6.42 in Applied SDEs.

dispersion(time, state, **kwargs)

Evaluates l(t, x(t)) = L(t).

drift(time, state, **kwargs)

Evaluates f(t, x(t)) = F(t) x(t) + u(t).

jacobian(time, state, **kwargs)

maps t -> F(t)

precond2nordsieck(step)

Computes preconditioner inspired by Nordsieck.

Computes the matrix \(P\) given by

\[P = I_d \otimes diag (1, h, h^2, ..., h^q)\]

as well as its inverse \(P^{-1}\).

Parameters:step (float) – Step size \(h\) used for preconditioning. If \(h\) is so small that \(h^q! < 10^{-15}\), it is being set to \(h = (\cdot 10^{-15})^{1/q}\).
Returns:
  • precond (np.ndarray, shape=(d(q+1), d(q+1))) – Preconditioner matrix \(P\).
  • invprecond (np.ndarray, shape=(d(q+1), d(q+1))) – Inverse preconditioner matrix \(P^{-1}\).
proj2coord(coord)

Projection matrix to \(i\)-th coordinates.

Computes the matrix

\[H_i = \left[ I_d \otimes e_i \right] P^{-1},\]

where \(e_i\) is the \(i\)-th unit vector, that projects to the \(i\)-th coordinate of a vector. If the ODE is multidimensional, it projects to each of the \(i\)-th coordinates of each ODE dimension.

Parameters:coord (int) – Coordinate index \(i\) which to project to. Expected to be in range \(0 \leq i \leq q + 1\).
Returns:Projection matrix \(H_i\).
Return type:np.ndarray, shape=(d, d*(q+1))
sample(start, stop, step, initstate, **kwargs)

Samples from initstate at start to stop with stepsize step.

Start, stop and step lead to a np.arange-like interface. Returns a single element at the end of the time, not the entire array!