Linear Gaussian filtering and smoothing (continuous-discrete)

Provided is an example of linear state-space models on which one can perform Bayesian filtering and smoothing in order to obtain a posterior distribution over a latent state trajectory based on noisy observations. In order to understand the theory behind these methods in detail we refer to [1] and [2].

References: > [1] Särkkä, Simo, and Solin, Arno. Applied Stochastic Differential Equations. Cambridge University Press, 2019.
> > [2] Särkkä, Simo. Bayesian Filtering and Smoothing. Cambridge University Press, 2013.
import numpy as np

import probnum as pn
from probnum import filtsmooth, randvars, randprocs
from probnum.problems import TimeSeriesRegressionProblem
rng = np.random.default_rng(seed=123)
# Make inline plots vector graphics instead of raster graphics
%matplotlib inline
from IPython.display import set_matplotlib_formats

set_matplotlib_formats("pdf", "svg")

# Plotting
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec"../../probnum.mplstyle")
/tmp/ipykernel_125705/ DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`
  set_matplotlib_formats("pdf", "svg")

Linear Continuous-Discrete State-Space Model: Ornstein-Uhlenbeck Process

Now, we have a look at continuous dynamics. We assume that there is a continuous process that defines the dynamics of our latent space from which we collect discrete linear-Gaussian measurements (as above). Only the dynamics model becomes continuous. In particular, we formulate the dynamics as a stochastic process in terms of a linear time-invariant stochastic differential equation (LTISDE). We refer to [1] for more details. Consider matrices \(\boldsymbol{F} \in \mathbb{R}^{d \times d}\), \(\boldsymbol{L} \in \mathbb{R}^{s \times d}\) and \(H \in \mathbb{R}^{m \times d}\) where \(d\) is the state dimension and \(m\) is the dimension of the measurements. We define the following continuous-discrete state-space model:

Let \(x(t_0) \sim \mathcal{N}(\mu_0, \Sigma_0)\).

\[\begin{split}\begin{align} d\boldsymbol{x} &= \boldsymbol{F} \, \boldsymbol{x} \, dt + \boldsymbol{L} \, d \boldsymbol{\omega} \\ \boldsymbol{y}_k &\sim \mathcal{N}(\boldsymbol{H} \, \boldsymbol{x}(t_k), \boldsymbol{R}), \qquad k = 1, \dots, K \end{align}\end{split}\]

where \(\boldsymbol{\omega} \in \mathbb{R}^s\) denotes a vector of driving forces (often Brownian Motion).

Note that this can be generalized to a linear time-varying state-space model, as well. Then \(\boldsymbol{F}\) is a function \(\mathbb{T} \rightarrow \mathbb{R}^{d \times d}\), \(\boldsymbol{L}\) is a function \(\mathbb{T} \rightarrow \mathbb{R}^{s \times d}\), and \(H\) is a function \(\mathbb{T} \rightarrow \mathbb{R}^{m \times d}\) where \(\mathbb{T}\) is the “time dimension”. In the following example, however, we consider a LTI SDE, namely, the Ornstein-Uhlenbeck Process from which we observe discrete linear Gaussian measurements.

Define State-Space Model

I. Continuous Dynamics Model: Linear, Time-Invariant Stochastic Differential Equation (LTISDE)

state_dim = 1
observation_dim = 1
delta_t = 0.2
# Define Linear, time-invariant Stochastic Differential Equation that models
# the (scalar) Ornstein-Uhlenbeck Process
drift_constant = 0.21
dispersion_constant = np.sqrt(0.5)
drift = -drift_constant * np.eye(state_dim)
force = np.zeros(state_dim)
dispersion = dispersion_constant * np.eye(state_dim)

The continuous counterpart to the discrete LTI Gaussian model is provided via the LTISDE class.

# Create dynamics model
dynamics_model = randprocs.markov.continuous.LTISDE(

II. Discrete Measurement Model: Linear, Time-Invariant Gaussian Measurements

measurement_marginal_variance = 0.1
measurement_matrix = np.eye(observation_dim, state_dim)
measurement_noise_matrix = measurement_marginal_variance * np.eye(observation_dim)

As above, the measurement model is discrete, LTI Gaussian. Only the dymanics are continuous (i.e. continuous-discrete).

noise = randvars.Normal(mean=np.zeros(observation_dim), cov=measurement_noise_matrix)
measurement_model = randprocs.markov.discrete.LTIGaussian(

III. Initial State Random Variable

mu_0 = 10.0 * np.ones(state_dim)
sigma_0 = np.eye(state_dim)
initial_state_rv = randvars.Normal(mean=mu_0, cov=sigma_0)
prior_process = randprocs.markov.MarkovProcess(
    transition=dynamics_model, initrv=initial_state_rv, initarg=0.0

Generate Data for the State-Space Model

Next, sample both latent states and noisy observations from the specified state-space model.

time_grid = np.arange(0.0, 10.0, step=delta_t)
latent_states, observations = randprocs.markov.utils.generate_artificial_measurements(
regression_problem = TimeSeriesRegressionProblem(
    measurement_models=[measurement_model] * len(time_grid),

Kalman Filtering

In fact, since we still consider a linear model, we can apply Kalman Filtering in this case again. According to Section 10 in [1], the moments of the filtering posterior in the continuous-discrete case are solutions to linear differential equations, which probnum solves for us when invoking the <Kalman_object>.filtsmooth(...) method.

I. Kalman Filter

kalman_filter = filtsmooth.gaussian.Kalman(prior_process)

II. Perform Kalman Filtering + Rauch-Tung-Striebel Smoothing

state_posterior, _ = kalman_filter.filtsmooth(regression_problem)
The method filtsmooth returns a KalmanPosterior object which provides convenience functions for e.g. sampling and prediction. We can also extract the just computed posterior smoothing state variables by querying the .state_rvs property.
This yields a list of Gaussian Random Variables from which we can extract the statistics in order to visualize them.
grid = np.linspace(0, 11, 500)

posterior_state_rvs = state_posterior(
)  # List of <num_time_points> Normal Random Variables
posterior_state_means = posterior_state_rvs.mean.squeeze()  # Shape: (num_time_points, )
posterior_state_covs = posterior_state_rvs.cov  # Shape: (num_time_points, )

samples = state_posterior.sample(rng=rng, size=3, t=grid)

Visualize Results

state_fig = plt.figure()

ax = state_fig.add_subplot()

# Plot means
ax.plot(grid, posterior_state_means, label="posterior mean")

# Plot samples
for smp in samples:
        smp[:, 0],

# Plot marginal standard deviations
std_x = np.sqrt(np.abs(posterior_state_covs)).squeeze()
    posterior_state_means - 1.96 * std_x,
    posterior_state_means + 1.96 * std_x,
    label="1.96 marginal stddev",
ax.scatter(time_grid, observations, marker=".", label="measurements")
# Add labels etc.

# These two lines just remove duplicate labels (caused by the samples) from the legend
handles, labels = ax.get_legend_handles_labels()
by_label = dict(zip(labels, handles))

    by_label.values(), by_label.keys(), loc="center left", bbox_to_anchor=(1, 0.5)