Artificial Intelligence: A Modern Approach, 4th Global ed. by Stuart Russell and Peter Norvig contains the following footnote on page 480 of chapter 14:
Uncertainty over continuous time can be modeled by stochastic differential equations (SDEs). The models studied in this chapter can be viewed as discrete-time approximations to SDEs.
The chapter is on probabilistic reasoning over time and covers Hidden Markov Models, Kalman Filters and Dynamic Bayesian Networks.
In light of the footnote; could someone please explain the connections these models have to SDEs and/or possibly provide some relevant references?