Abstract
A lot of models are included in the optimal stopping problem. Here, we treat the job search problem as a clue of the model to maximize the expected value. In addition, it thinks about a sequential stochastic assignment problem and a job search problem on a partially observable Markov chain. The job search is a problem for a sequence of the random variables appearing one by one, to select one from among them, and maximizing the expected value. It is possible to extend variously, and it explains a sequential stochastic assignment problem as the one. On the other hand, though it is not possible to know the state of the Markov chain directly, information is obtained through the information process, this process is said the partially observable Markov chain. It thinks about the Bayesian learning procedure for this chain. The order is introduced into the information space of use the likelihood ratio, and make a preparation to analyze the property of the sequential decision problems in this Markov chain and necessary properties are brought together for that. Finally, it thinks about the job search in this Markov chain as a sequential decision problem of incomplete information.
