Hidden Markov Models have come into vogue in recent years in various fields. Notably automatic speech recognition. An HMM is useful in a Bayesian context, where you have to work back from some observations to discern an underlying probability model that is supposedly generating those observations. Often in the presence of noise. Well, it turns out that this general description can also be applied to financial models, which is the book's subject.
Various specific models are tackled. Including the seminal Black-Scholes, where the security market is modelled as a Markov modulated Brownian. Typically, the maths in the book uses sophisticated probabilistic analysis and often assuming Markov processes. As an aside, if your field is electrical engineering or information theory, where you might have used Markov processes, then your background should suffice if you want to migrate to finance. It's not that different, at a certain conceptual level.
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