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Journal ArticleDOI

Dynamic Programming and Stochastic Control.

S. Christian Albright, +1 more
- 01 Jun 1979 - 
- Vol. 74, Iss: 366, pp 510
TLDR
In this article, the authors propose a method to find the book that you love to read first or find an interesting book that will make you want to read, but not a book.
Abstract
What do you do to start reading dynamic programming and stochastic control? Searching the book that you love to read first or find an interesting book that will make you want to read? Everybody has difference with their reason of reading a book. Actuary, reading habit must be from earlier. Many people may be love to read, but not a book. It's not fault. Someone will be bored to open the thick book with small words to read. In more, this is the real condition. So do happen probably with this dynamic programming and stochastic control.

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Citations
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Book

Recursive Macroeconomic Theory

TL;DR: In this paper, an introduction to recursive methods for dynamic macroeconomics is presented, including standard applications such as asset pricing, and advanced material, including analyses of reputational mechanisms and contract design.
Journal ArticleDOI

Formulating and estimating dynamic linear rational expectations models

TL;DR: In this paper, the authors describe methods for conveniently formulating and estimating dynamic linear econometric models under the hypothesis of rational expectations and derive an econometrically convenient formula for the cross-equation rational expectations restrictions.
Journal ArticleDOI

On foraging time allocation in a stochastic environment

Thomas Caraco
- 01 Feb 1980 - 
TL;DR: It is suggested that maximization of expected utility may be a useful optimization criterion for ecological problems, and the common notion of feeding preferences is extended over the probability distributions of net energetic benefits associated with available resources.
Journal ArticleDOI

FIRM: Sampling-based feedback motion-planning under motion uncertainty and imperfect measurements

TL;DR: FIRM is introduced as an abstract framework, a multi-query approach for planning under uncertainty which is a belief-space variant of probabilistic roadmap methods and the so-called SLQG-FIRM, a concrete instantiation of FIRM that focuses on kinematic systems and then extends to dynamical systems by sampling in the equilibrium space.
Journal ArticleDOI

Markov Decision Processes: A Tool for Sequential Decision Making under Uncertainty:

TL;DR: This tutorial provides a tutorial on the construction and evaluation of Markov decision processes (MDPs), which are powerful analytical tools used for sequential decision making under uncertainty that have been widely used in many industrial and manufacturing applications but are underutilized in medical decision making (MDM).