Optimal stopping of bi-Markov processes

2005 ◽  
pp. 172-205 ◽  
Author(s):  
G. Mazziotto
Keyword(s):  
Markov Processes ◽  
Optimal Stopping

scholarly journals Optimal stopping for partially observed piecewise-deterministic Markov processes

2013 ◽  
Vol 123 (8) ◽  
pp. 3201-3238 ◽  
Author(s):  
Adrien Brandejsky ◽  
Benoîte de Saporta ◽  
François Dufour
Keyword(s):  
Markov Processes ◽  
Optimal Stopping ◽  
Piecewise Deterministic Markov Processes ◽  
Partially Observed

Optimal Stopping Games for Markov Processes

2008 ◽  
Vol 47 (2) ◽  
pp. 684-702 ◽  
Author(s):  
Erik Ekström ◽  
Goran Peskir
Keyword(s):  
Markov Processes ◽  
Optimal Stopping ◽  
Stopping Games

scholarly journals Partially observed optimal stopping problem for discrete-time Markov processes

4OR ◽  
2016 ◽  
Vol 15 (3) ◽  
pp. 277-302 ◽  
Author(s):  
Benoîte de Saporta ◽  
François Dufour ◽  
Christophe Nivot
Keyword(s):  
Markov Processes ◽  
Discrete Time ◽  
Optimal Stopping ◽  
Optimal Stopping Problem ◽  
Partially Observed

scholarly journals On the excessive functions of the step Markov processes and optimal stopping rules

1967 ◽  
Vol 7 (1) ◽  
pp. 37-44
Author(s):  
B. Grigelionis
Keyword(s):  
Markov Processes ◽  
Optimal Stopping ◽  
Stopping Rules ◽  
Excessive Functions ◽  
Optimal Stopping Rules

The abstracts (in two languages) can be found in the pdf file of the article. Original author name(s) and title in Russian and Lithuanian: Б. Григелионис. Об эксцессивных функциях и оптимальных правилах остановки ступенчатых марковских процессов B. Grigelionis. Apie laiptuotų Markovo procesų ekscesyvines funkcijas ir optimalias sustabdymo taisykles

scholarly journals Optimal stopping for measure-valued piecewise deterministic Markov processes

2020 ◽  
Vol 57 (2) ◽  
pp. 497-512
Author(s):  
Bertrand Cloez ◽  
Benoîte de Saporta ◽  
Maud Joubaud
Keyword(s):  
Markov Processes ◽  
Optimal Stopping ◽  
Small Population ◽  
Individual Characteristics ◽  
Locally Finite ◽  
Counter Example ◽  
Random Horizon ◽  
Piecewise Deterministic Markov Processes ◽  
Point Measure ◽  
Finite Measures

AbstractThis paper investigates the random horizon optimal stopping problem for measure-valued piecewise deterministic Markov processes (PDMPs). This is motivated by population dynamics applications, when one wants to monitor some characteristics of the individuals in a small population. The population and its individual characteristics can be represented by a point measure. We first define a PDMP on a space of locally finite measures. Then we define a sequence of random horizon optimal stopping problems for such processes. We prove that the value function of the problems can be obtained by iterating some dynamic programming operator. Finally we prove via a simple counter-example that controlling the whole population is not equivalent to controlling a random lineage.

General optimal stopping theorems for semi-Markov processes

1993 ◽  
Vol 25 (4) ◽  
pp. 825-846 ◽  
Author(s):  
Frans A. Boshuizen ◽  
José M. Gouweleeuw
Keyword(s):  
Markov Processes ◽  
Optimal Stopping ◽  
Stopping Time ◽  
General Setting ◽  
The State ◽  
Shock Models ◽  
Special Cases ◽  
Optimal Stopping Problems ◽  
The Times ◽  
Semi Markov Processes

In this paper, optimal stopping problems for semi-Markov processes are studied in a fairly general setting. In such a process transitions are made from state to state in accordance with a Markov chain, but the amount of time spent in each state is random. The times spent in each state follow a general renewal process. They may depend on the present state as well as on the state into which the next transition is made.Our goal is to maximize the expected net return, which is given as a function of the state at time t minus some cost function. Discounting may or may not be considered. The main theorems (Theorems 3.5 and 3.11) are expressions for the optimal stopping time in the undiscounted and discounted case. These theorems generalize results of Zuckerman [16] and Boshuizen and Gouweleeuw [3]. Applications are given in various special cases.The results developed in this paper can also be applied to semi-Markov shock models, as considered in Taylor [13], Feldman [6] and Zuckerman [15].

Sign in / Sign up

Export Citation Format

Share Document