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Misplaced figure reference here #368

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11 changes: 0 additions & 11 deletions markdown/15-Probabilistic-Reasoning-Over-Time/exercises/ex_11/question.md
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Expand Up @@ -14,14 +14,3 @@ and follow it with probability .7 on each step; the robot switches to a
randomly selected new heading with probability .3 (or with probability 1
if it encounters a wall). Implement this as an HMM and do filtering to
track the robot. How accurately can we track the robot’s path?

<figure>
<img src="https://aimacode.github.io/aima-exercises/figures/switching-kf.svg" alt="switching-kf-figure" id="switching-kf-figure" style="width:100%">
<figcaption><center><b>A Bayesian network representation of a switching Kalman filter. The switching variable $S_t$ is a discrete state variable whose value determines
the transition model for the continuous state variables $\textbf{X}_t$.
For any discrete state $\textit{i}$, the transition model
$\textbf{P}(\textbf{X}_{t+1}|\textbf{X}_t,S_t= i)$ is a linear Gaussian model, just as in a
regular Kalman filter. The transition model for the discrete state,
$\textbf{P}(S_{t+1}|S_t)$, can be thought of as a matrix, as in a hidden
Markov model.</b></center></figcaption>
</figure>