Partially Observable Markov Decision Processes

In many applications the decision maker has only partial information about the state process, i.e. part of the state cannot be observed. Examples can be found in engineering, economics, statistics, speech recognition and learning theory among others. An important financial application is given when the drift of a stock price process is unobservable and hard to estimate.