DiscreteGaussianLinearModel

class probnum.filtsmooth.DiscreteGaussianLinearModel(dynamatfct, forcefct, diffmatfct)

Bases: probnum.filtsmooth.DiscreteGaussianModel

Linear version. g(t, x(t)) = G(t) x(t) + z(t).

Attributes Summary

ndim

Methods Summary

diffusionmatrix(time, **kwargs)	Evaluate S(t_i)
dynamics(time, state, **kwargs)	Evaluate g(t_i, x_i).
dynamicsmatrix(time, **kwargs)	Convenient access to dynamics matrix (alternative to “jacobian”).
force(time, **kwargs)
jacobian(time, state, **kwargs)	Evaluate Jacobian, d_x g(t_i, x_i), of g(t_i, x_i) w.r.t.
pdf(loc, time, state, **kwargs)	Evaluates “future” pdf p(x_t | x_s) at loc.
sample(time, state, **kwargs)	Samples x_{t} ~ p(x_{t} | x_{s}) as a function of t and x_s (plus additional parameters).

Attributes Documentation

ndim

Methods Documentation

diffusionmatrix(time, **kwargs)

Evaluate S(t_i)

dynamics(time, state, **kwargs)

Evaluate g(t_i, x_i).

dynamicsmatrix(time, **kwargs)

Convenient access to dynamics matrix (alternative to “jacobian”).

force(time, **kwargs)

jacobian(time, state, **kwargs)

Evaluate Jacobian, d_x g(t_i, x_i), of g(t_i, x_i) w.r.t. x_i.

pdf(loc, time, state, **kwargs)

Evaluates “future” pdf p(x_t | x_s) at loc.

sample(time, state, **kwargs)

Samples x_{t} ~ p(x_{t} | x_{s}) as a function of t and x_s (plus additional parameters).

In a discrete system, i.e. t = s + 1, s in mathbb{N}

In an ODE solver setting, one of the additional parameters would be the step size.