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Assumed Distribution

The Experts below are selected from a list of 237 Experts worldwide ranked by ideXlab platform

Srinivas Shakkottai – 1st expert on this subject based on the ideXlab platform

  • Mean Field Games in Nudge Systems for Societal Networks
    arXiv: Computer Science and Game Theory, 2015
    Co-Authors: Jian Li, Xinbo Geng, Hao Ming, Srinivas Shakkottai, Vijay G. Subramanian

    Abstract:

    We consider the general problem of resource sharing in societal networks, consisting of interconnected communication, transportation, energy and other networks important to the functioning of society. Participants in such network need to take decisions daily, both on the quantity of resources to use as well as the periods of usage. With this in mind, we discuss the problem of incentivizing users to behave in such a way that society as a whole benefits. In order to perceive societal level impact, such incentives may take the form of rewarding users with lottery tickets based on good behavior, and periodically conducting a lottery to translate these tickets into real rewards. We will pose the user decision problem as a mean field game (MFG), and the incentives question as one of trying to select a good mean field equilibrium (MFE). In such a framework, each agent (a participant in the societal network) takes a decision based on an Assumed Distribution of actions of his/her competitors, and the incentives provided by the social planner. The system is said to be at MFE if the agent’s action is a sample drawn from the Assumed Distribution. We will show the existence of such an MFE under different settings, and also illustrate how to choose an attractive equilibrium using as an example demand-response in energy networks.

  • A mean field game approach to scheduling in cellular systems
    IEEE INFOCOM 2014 – IEEE Conference on Computer Communications, 2014
    Co-Authors: Mayank Manjrekar, Vinod Ramaswamy, Srinivas Shakkottai

    Abstract:

    We study auction-theoretic scheduling in cellular networks using the idea of mean field equilibrium (MFE). Here, agents model their opponents through a Distribution over their action spaces and play the best response. The system is at an MFE if this action is itself a sample drawn from the Assumed Distribution. In our setting, the agents are smart phone apps that generate service requests, experience waiting costs, and bid for service from base stations. We show that if we conduct a second-price auction at each base station, there exists an MFE that would schedule the app with the longest queue at each time. The result suggests that auctions can attain the same desirable results as queue-length-based scheduling. We present results on the asymptotic convergence of a system with a finite number of agents to the mean field case, and conclude with simulation results illustrating the simplicity of computation of the MFE.

T.l. Landers – 2nd expert on this subject based on the ideXlab platform

  • iscretizing Approach for Stress/Strength Analvsis
    , 1996
    Co-Authors: T.l. Landers

    Abstract:

    Summary & Conclusions – This paper implements & evaluates a discretizing approach for estimating the reliability of systems for which complex functions define strength or stress and where the derivation of reliability exceed analytic techniques. The discretizing approach predicts system reliability with reasonably high accuracy. Specifically, there is little difference in the accuracy of predictions for three engineering problems when compared to simulation results. The reliability predictions are near the 95% confidence intervals of the simulation results and are best in the high reliability and low reliability regions. The small errors observed are attributed to the estimation errors of the discretizing approach. The mid-range reliability values (eg, 50% reliability) are not generally of interest in engineering applications, and even for these value, the errors are small. There is little improvement in increasing the number of points in the pmf from 3 to 6. Due to this small difference, 3 discretizing points are recommended for reliability predictions when computational ease is of concern and limited to 4 points when more accurate reliability predictions are required. This paper models three systems and evaluates the robustness (departures from Assumed Distributions) of the discretizing approach. The discretizing approach is not too sensitive to departures from the Assumed Distribution of the underlying random variables. Specifically, estimated reliability predictions using the discretizing approach are close to both the Gaussian & nearGaussian cases, but as more severe departures are encountered, only the high reliability regions are accurately estimated. Therefore, if we are concerned with high-reliability regions, this approach is effective, and as less reliable systems are analyzed, more attention should be dedicated to changing the design &an on the details of slight parameter adjustments.

  • A discretizing approach for stress/strength analysis
    IEEE Transactions on Reliability, 1996
    Co-Authors: J.r. English, T. Sargent, T.l. Landers

    Abstract:

    This paper implements and evaluates a discretizing approach for estimating the reliability of systems for which complex functions define strength or stress and where the derivation of reliability exceed analytic techniques. The discretizing approach predicts system reliability with reasonably high accuracy. Specifically, there is little difference in the accuracy of predictions for three engineering problems when compared to simulation results. The reliability predictions are near the 95% confidence intervals of the simulation results and are best in the high reliability and low reliability regions. The small errors observed are attributed to the estimation errors of the discretizing approach. The mid-range reliability values (e.g. 50% reliability) are not generally of interest in engineering applications, and even for these value, the errors are small. There is little improvement in increasing the number of points in the pmf from 3 to 6. Due to this small difference, 3 discretizing points are recommended for reliability predictions when computational ease is of concern and limited to 4 points when more accurate reliability predictions are required. This paper models three systems and evaluates the robustness (departures from Assumed Distributions) of the discretizing approach. The discretizing approach is not too sensitive to departures from the Assumed Distribution of the underlying random variables regions are accurately estimated.

J N Wang – 3rd expert on this subject based on the ideXlab platform

  • an approach for determining an appropriate Assumed Distribution of fatigue life under limited data
    Reliability Engineering & System Safety, 2000
    Co-Authors: Y X Zhao, J N Wang

    Abstract:

    Abstract The case of limited data implies that some unknown uncertainties may be involved in fatigue reliability analysis. For the sake of statistical convenience, for consistency with the relevant physical arguments and, most importantly, to ensure the safety in design evaluation, an approach is developed to determine an appropriate Distribution, from four possible Assumed Distributions—three-parameter Weibull, two-parameter Weibull, lognormal and extreme maximum-value Distributions. The approach makes allowance for consistency with the fatigue physics and checking tail fit effects. An application to nine groups of fatigue life data of 16Mn steel (Chinese steel) welded plate specimens shows that the lognormal Distribution and the extreme maximum-value Distribution may be the appropriate Distributions of the fatigue life under limited data.