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Displaying similar documents to “Discounted optimal stopping for maxima in diffusion models with finite horizon.”

A consumption-investment problem modelled as a discounted Markov decision process

Hugo Cruz-Suárez, Raúl Montes-de-Oca, Gabriel Zacarías (2011)

Kybernetika

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In this paper a problem of consumption and investment is presented as a model of a discounted Markov decision process with discrete-time. In this problem, it is assumed that the wealth is affected by a production function. This assumption gives the investor a chance to increase his wealth before the investment. For the solution of the problem there is established a suitable version of the Euler Equation (EE) which characterizes its optimal policy completely, that is, there are provided...

Average cost Markov control processes with weighted norms: value iteration

Evgueni Gordienko, Onésimo Hernández-Lerma (1995)

Applicationes Mathematicae

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This paper shows the convergence of the value iteration (or successive approximations) algorithm for average cost (AC) Markov control processes on Borel spaces, with possibly unbounded cost, under appropriate hypotheses on weighted norms for the cost function and the transition law. It is also shown that the aforementioned convergence implies strong forms of AC-optimality and the existence of forecast horizons.

Bayes optimal stopping of a homogeneous poisson process under linex loss function and variation in the prior

Marek Męczarski, Ryszard Zieliński (1997)

Applicationes Mathematicae

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A homogeneous Poisson process (N(t),t ≥ 0) with the intensity function m(t)=θ is observed on the interval [0,T]. The problem consists in estimating θ with balancing the LINEX loss due to an error of estimation and the cost of sampling which depends linearly on T. The optimal T is given when the prior distribution of θ is not uniquely specified.

Monotone optimal policies in discounted Markov decision processes with transition probabilities independent of the current state: existence and approximation

Rosa María Flores-Hernández (2013)

Kybernetika

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In this paper there are considered Markov decision processes (MDPs) that have the discounted cost as the objective function, state and decision spaces that are subsets of the real line but are not necessarily finite or denumerable. The considered MDPs have a cost function that is possibly unbounded, and dynamic independent of the current state. The considered decision sets are possibly non-compact. In the context described, conditions to obtain either an increasing or decreasing optimal...