Sequential Updating Simulation

2006 ◽  
pp. 307-318
Author(s):  
R. Froidevaux
Keyword(s):  
Sequential Updating

Sequential updating algorithms for grand canonical Monte Carlo simulations

2007 ◽  
Vol 105 (2-3) ◽  
pp. 231-238 ◽  
Author(s):  
Ruichao Ren ◽  
C. J. O'keeffe ◽  
G. Orkoulas
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Grand Canonical Monte Carlo ◽  
Sequential Updating ◽  
Grand Canonical

Sequential updating of conditional probabilities on directed graphical structures

Networks ◽  
1990 ◽  
Vol 20 (5) ◽  
pp. 579-605 ◽  
Author(s):  
David J. Spiegelhalter ◽  
Steffen L. Lauritzen
Keyword(s):  
Conditional Probabilities ◽  
Sequential Updating

scholarly journals Sequential Updating of Quantitative Requirements for Increased Flexibility in Robust Systems Design

2019 ◽  
Vol 1 (1) ◽  
pp. 3531-3540
Author(s):  
Matthias Funk ◽  
Marcus Jautze ◽  
Manfred Strohe ◽  
Markus Zimmermann
Keyword(s):  
Information Exchange ◽  
Systems Design ◽  
Distributed Teams ◽  
Limited Information ◽  
Design Variables ◽  
Decision Variables ◽  
Subsystem Design ◽  
Sequential Updating ◽  
Early Decision ◽  
Independent Design

AbstractIn early development stages of complex systems, interacting subsystems (including components) are often designed simultaneously by distributed teams with limited information exchange. Distributed development becomes possible by assigning teams independent design goals expressed as quantitative requirements equipped with tolerances to provide flexibility for design: so-called solution-spaces are high-dimensional sets of permissible subsystem properties on which requirements on the system performance are satisfied. Edges of box-shaped solution spaces are permissible intervals serving as decoupled (mutually independent) requirements for subsystem design variables. Unfortunately, decoupling often leads to prohibitively small intervals. In so-called solution-compensation spaces, permissible intervals for early-decision variables are increased by a compensation mechanism using late-decision variables. This paper presents a multi-step development process where groups of design variables successively change role from early-decision to late-decision type in order to maximize flexibility. Applying this to a vehicle chassis design problem demonstrates the effectiveness of the approach.

Long trial durations normalise the interference effect and sequential updating during healthy aging

2014 ◽  
Vol 153 ◽  
pp. 169-178 ◽  
Author(s):  
D. Aisenberg ◽  
A. Sapir ◽  
G. d'Avossa ◽  
A. Henik
Keyword(s):  
Interference Effect ◽  
Healthy Aging ◽  
Sequential Updating

scholarly journals The odds algorithm based on sequential updating and its performance

2009 ◽  
Vol 41 (01) ◽  
pp. 131-153 ◽  
Author(s):  
F. Thomas Bruss ◽  
Guy Louchard
Keyword(s):  
Optimal Stopping ◽  
Stopping Time ◽  
Stopping Times ◽  
Selection Problems ◽  
Real World Applications ◽  
Sequential Updating ◽  
Indicator Functions ◽  
Sequential Information ◽  
And Performance ◽  
Independent Indicator

LetI1,I2,…,Inbe independent indicator functions on some probability spaceWe suppose that these indicators can be observed sequentially. Furthermore, letTbe the set of stopping times on (Ik),k=1,…,n, adapted to the increasing filtrationwhereThe odds algorithm solves the problem of finding a stopping time τ ∈Twhich maximises the probability of stopping on the lastIk=1, if any. To apply the algorithm, we only need the odds for the events {Ik=1}, that is,rk=pk/(1-pk), whereThe goal of this paper is to offer tractable solutions for the case where thepkare unknown and must be sequentially estimated. The motivation is that this case is important for many real-world applications of optimal stopping. We study several approaches to incorporate sequential information. Our main result is a new version of the odds algorithm based on online observation and sequential updating. Questions of speed and performance of the different approaches are studied in detail, and the conclusiveness of the comparisons allows us to propose always using this algorithm to tackle selection problems of this kind.

DEVELOPING FUZZY BAYESIAN GAME MODEL FOR OPTIMIZING NEGOTIATION PRICE

2014 ◽  
Vol 13 (04) ◽  
pp. 1450022
Author(s):  
SOU-SEN LEU ◽  
PHAM VU HONG SON ◽  
P. E. JUI-SHENG CHOU ◽  
PHAM THI HONG NHUNG
Keyword(s):  
Game Model ◽  
Bayesian Game ◽  
Negotiation Strategy ◽  
Factors Affecting ◽  
Price Negotiation ◽  
Comprehensive View ◽  
Proposed Model ◽  
Sequential Updating ◽  
Contractual Agreement

Construction procurement is a key business where price negotiation is commonly required to reach final contractual agreement. However, even simple negotiations often result in infeasible agreements. The uncertain and limited supplier information as well as complex correlations among various factors affecting supplier behaviors make the contractor difficult to decide the appropriate offer price (OP) and vice versa. This study proposes a novel Fuzzy Bayesian Game Model (FBGM) for improving the prediction effectiveness of negotiation behaviors. The performance of the proposed FBGM was evaluated in the case where an agent uses the counter-OP of an opponent to learn the negotiation strategy of the opponent. The validation analysis shows that the sequential updating process of FBGM significantly improves the estimation ability of negotiators. The proposed model also gives a comprehensive view of negotiation scenarios by considering all possible negotiation cases. Using FBGM, negotiators can apply flexible strategies to optimize their own profit with a reasonable negotiation time.

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