Gaussian Process and Kalman Filter

ongoing

Recursive linear regression

Suppose we are interested in solving a linear regression problem

\begin{align}\notag Y_n=X_n^\intercal \beta + \epsilon, \end{align}

where $\epsilon$ is a Gaussian noise with mean $0$. Together with a prior $\mathbb{N}(0, \frac{1}{2}\lambda I_n)$, we have

\begin{align}\label{ori_linear_reg} \widehat \beta_n=\big(X_n^\intercal X_n + \lambda I_n\big)^{-1} X_n^\intercal Y_n. \end{align}

It is worth noting that X and Y often comes as a streaming set of data. As such, it is expensive to solve \eqref{ori_linear_reg} everytime new data comes in. Instead, we consider an recursive algorithm

Given an existing posterior conditioned on measurements $1,2,\cdots, k-1$.

\begin{align}\notag p(\theta|y_{1:k-1})=\mathbb{N}(m_{k-1}, P_{k-1}). \end{align}

Now when a new measurement comes, the likelihood follows \begin{align}\notag p(y_k|\theta)=\mathbb{N}(H_k \theta, \sigma^2). \end{align}

Using Bayes rule, we have

\[\begin{align}\notag p(\theta|y_{1:k})&\propto p(y_k|\theta) p(\theta|y_{1:k-1})\\\notag &\propto \mathbb{N}(\theta|m_k, P_k),\\\notag \end{align}\]

where the parameters follow

\[\begin{align}\notag m_k&=\bigg[P_{k-1}^{-1} +\frac{1}{\sigma^2} H_k^\intercal H_k\bigg]^{-1}\bigg[\frac{1}{\sigma^2} H_k^\intercal y_k + P_{k-1}^{-1} m_{k-1}\bigg],\\ P_k&=\bigg[P_{k-1}^{-1} +\frac{1}{\sigma^2} H_k^\intercal H_k\bigg]^{-1}.\notag\\ \end{align}\]

\begin{align}\notag \end{align}

Recall that the matrix inversion formula follows that

\[\begin{align}\notag (A+BD)^{-1}=A^{-1} - A^{-1} B(I+DA^{-1}B)^{-1}DA^{-1}. \end{align}\]

The covariance update follows that

\[\begin{align}\notag P_k=P_{k-1}-P_{k-1}H_k^\intercal \big[H_k P_{k-1} H_k^\intercal +\sigma^2 I\big]^{-1} H_k P_{k-1}. \end{align}\]

Now including auxiliary variables $S_k$ and $K_k$, the updates follow that

\[\begin{align} S_k &= H_k P_{k-1} H_k^\intercal +\sigma^2 I \notag\\ K_k &= P_{k-1} H_k^\intercal S_k^{-1} \notag\\ m_k &= m_{k-1} + K_k \bigg[y_k-H_k m_{k-1}\bigg] \notag\\ P_k &= P_{k-1}-K_k S_k K_k^\intercal \notag\\ \end{align}\]

where the update resembles the update equestions of the Kalman filter.

Kalman Filter

Gaussian Processes

An Introduction to Gaussian Processes for the Kalman Filter Expert

KALMAN FILTERING AND SMOOTHING SOLUTIONS TO TEMPORAL GAUSSIAN PROCESS REGRESSION MODELS.

TBD

(Särkkä, 2023) (Rasmussen & Williams, 2006)

(Reece & Roberts, 2010)

  1. Särkkä, S. (2023). Bayesian Filtering and Smoothing. Cambridge University Press.
  2. Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.
  3. Reece, S., & Roberts, S. (2010). An Introduction to Gaussian Processes for the Kalman Filter Expert. International Conference on Information Fusion.