Problem:

I'm working in reliability field and have seen papers written on the topic like process of failures when systems are functioning under unobservable (or observable) Markov-like environment, i.e. probability to fail is dependent on the state of environment. This state is described as discrete-state discrete-time homogeneous Markov chain. In mathematical notation it would be like this:

$ P\left [ X_{t}=k | Y_{t}=i \right ]=\pi _{k}\left ( i \right ) $;

where
$ X _{t} $ is a binary random failure process with possible states 0 (failed) and 1 (working). And $Y _{t}$ is a Markov process at the moment $t$ being in the $i ^{th}$ state.

My question:

Is it possible (or even reasonable) to extend mentioned model? For example, from discrete-state to continuous-state Markov model? Is there any literature (I havent found yet) about continuous **stochastic conditional processes**. I suppose it is not so trivial, because for continuouse stochastic process statements like $Y_{t}=i$ are meaningless.