716 research outputs found

    Monte Carlo optimization of decentralized estimation networks over directed acyclic graphs under communication constraints

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    Motivated by the vision of sensor networks, we consider decentralized estimation networks over bandwidth–limited communication links, and are particularly interested in the tradeoff between the estimation accuracy and the cost of communications due to, e.g., energy consumption. We employ a class of in–network processing strategies that admits directed acyclic graph representations and yields a tractable Bayesian risk that comprises the cost of communications and estimation error penalty. This perspective captures a broad range of possibilities for processing under network constraints and enables a rigorous design problem in the form of constrained optimization. A similar scheme and the structures exhibited by the solutions have been previously studied in the context of decentralized detection. Under reasonable assumptions, the optimization can be carried out in a message passing fashion. We adopt this framework for estimation, however, the corresponding optimization scheme involves integral operators that cannot be evaluated exactly in general. We develop an approximation framework using Monte Carlo methods and obtain particle representations and approximate computational schemes for both the in–network processing strategies and their optimization. The proposed Monte Carlo optimization procedure operates in a scalable and efficient fashion and, owing to the non-parametric nature, can produce results for any distributions provided that samples can be produced from the marginals. In addition, this approach exhibits graceful degradation of the estimation accuracy asymptotically as the communication becomes more costly, through a parameterized Bayesian risk

    Monte Carlo optimization approach for decentralized estimation networks under communication constraints

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    We consider designing decentralized estimation schemes over bandwidth limited communication links with a particular interest in the tradeoff between the estimation accuracy and the cost of communications due to, e.g., energy consumption. We take two classes of in–network processing strategies into account which yield graph representations through modeling the sensor platforms as the vertices and the communication links by edges as well as a tractable Bayesian risk that comprises the cost of transmissions and penalty for the estimation errors. This approach captures a broad range of possibilities for “online” processing of observations as well as the constraints imposed and enables a rigorous design setting in the form of a constrained optimization problem. Similar schemes as well as the structures exhibited by the solutions to the design problem has been studied previously in the context of decentralized detection. Under reasonable assumptions, the optimization can be carried out in a message passing fashion. We adopt this framework for estimation, however, the corresponding optimization schemes involve integral operators that cannot be evaluated exactly in general. We develop an approximation framework using Monte Carlo methods and obtain particle representations and approximate computational schemes for both classes of in–network processing strategies and their optimization. The proposed Monte Carlo optimization procedures operate in a scalable and efficient fashion and, owing to the non-parametric nature, can produce results for any distributions provided that samples can be produced from the marginals. In addition, this approach exhibits graceful degradation of the estimation accuracy asymptotically as the communication becomes more costly, through a parameterized Bayesian risk

    Graphical model-based approaches to target tracking in sensor networks: an overview of some recent work and challenges

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    Sensor Networks have provided a technology base for distributed target tracking applications among others. Conventional centralized approaches to the problem lack scalability in such a scenario where a large number of sensors provide measurements simultaneously under a possibly non-collaborating environment. Therefore research efforts have focused on scalable, robust, and distributed algorithms for the inference tasks related to target tracking, i.e. localization, data association, and track maintenance. Graphical models provide a rigorous tool for development of such algorithms by modeling the information structure of a given task and providing distributed solutions through message passing algorithms. However, the limited communication capabilities and energy resources of sensor networks pose the additional difculty of considering the tradeoff between the communication cost and the accuracy of the result. Also the network structure and the information structure are different aspects of the problem and a mapping between the physical entities and the information structure is needed. In this paper we discuss available formalisms based on graphical models for target tracking in sensor networks with a focus on the aforementioned issues. We point out additional constraints that must be asserted in order to achieve further insight and more effective solutions

    Monte Carlo optimization approach for decentralized estimation networks under communication constraints

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    We consider designing decentralized estimation schemes over bandwidth limited communication links with a particular interest in the tradeoff between the estimation accuracy and the cost of communications due to, e.g., energy consumption. We take two classes of in–network processing strategies into account which yield graph representations through modeling the sensor platforms as the vertices and the communication links by edges as well as a tractable Bayesian risk that comprises the cost of transmissions and penalty for the estimation errors. This approach captures a broad range of possibilities for “online” processing of observations as well as the constraints imposed and enables a rigorous design setting in the form of a constrained optimization problem. Similar schemes as well as the structures exhibited by the solutions to the design problem has been studied previously in the context of decentralized detection. Under reasonable assumptions, the optimization can be carried out in a message passing fashion. We adopt this framework for estimation, however, the corresponding optimization schemes involve integral operators that cannot be evaluated exactly in general. We develop an approximation framework using Monte Carlo methods and obtain particle representations and approximate computational schemes for both classes of in–network processing strategies and their optimization. The proposed Monte Carlo optimization procedures operate in a scalable and efficient fashion and, owing to the non-parametric nature, can produce results for any distributions provided that samples can be produced from the marginals. In addition, this approach exhibits graceful degradation of the estimation accuracy asymptotically as the communication becomes more costly, through a parameterized Bayesian risk

    An efficient Monte Carlo approach for optimizing decentralized estimation networks constrained by undirected topologies

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    We consider a decentralized estimation network subject to communication constraints such that nearby platforms can communicate with each other through low capacity links rendering an undirected graph. After transmitting symbols based on its measurement, each node outputs an estimate for the random variable it is associated with as a function of both the measurement and incoming messages from neighbors. We are concerned with the underlying design problem and handle it through a Bayesian risk that penalizes the cost of communications as well as estimation errors, and constraining the feasible set of communication and estimation rules local to each node by the undirected communication graph. We adopt an iterative solution previously proposed for decentralized detection networks which can be carried out in a message passing fashion under certain conditions. For the estimation case, the integral operators involved do not yield closed form solutions in general so we utilize Monte Carlo methods. We achieve an iterative algorithm which yields an approximation to an optimal decentralized estimation strategy in a person by person sense subject to such constraints. In an example, we present a quantification of the trade-off between the estimation accuracy and cost of communications using the proposed algorithm

    An efficient Monte Carlo approach for optimizing communication constrained decentralized estimation networks

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    We consider the design problem of a decentralized estimation network under communication constraints. The underlying low capacity links are modeled by introducing a directed acyclic graph where each node corresponds to a sensor platform. The operation of the platforms are constrained by the graph such that each node, based on its measurement and incoming messages from parents, produces a local estimate and outgoing messages to children. A Bayesian risk that captures both estimation error penalty and cost of communications, e.g. due to consumption of the limited resource of energy, together with constraining the feasible set of strategies by the graph, yields a rigorous problem definition. We adopt an iterative solution that converges to an optimal strategy in a person-byperson sense previously proposed for decentralized detection networks under a team theoretic investigation. Provided that some reasonable assumptions hold, the solution admits a message passing interpretation exhibiting linear complexity in the number of nodes. However, the corresponding expressions in the estimation setting contain integral operators with no closed form solutions in general. We propose particle representations and approximate computational schemes through Monte Carlo methods in order not to compromise model accuracy and achieve an optimization method which results in an approximation to an optimal strategy for decentralized estimation networks under communication constraints. Through an example, we present a quantification of the trade-off between the estimation accuracy and the cost of communications where the former degrades as the later is increased

    The effect of health expenditures on economic growth: a panel regression analysis on OECD countries

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    Son yıllarda, ekonomik büyüme literatürüne yapılan teorik ve ampirik katkılar, ekonomik büyüme sürecinde beşeri sermayenin rolünü vurgulamaktadır. Ampirik çalışmaların büyük bir kısmı, genellikle eğitim ile ekonomik büyüme arasındaki ilişki üzerinde yoğunlaşmaktadır. Bu çalışmada ise, sağlığın ekonomik büyüme üzerindeki etkisi bir panel veri analizi ile test edilmektedir. Çalışma, 15 OECD ülkesine ilişkin 1990-2006 dönemi yıllık verilerini içerir. Analizlerde, diğer açıklayıcı değişkenlerin yanı sıra, kamu sağlık harcamalarının toplam sağlık harcamaları içindeki payı kullanılmıştır. Sağlık harcamaları ile ekonomik büyüme arasındaki ilişki, Havuzlanmış Regresyon Modeli çerçevesinde panel OLS metodu ile tahmin edilmiştir. Ampirik sonuçlara göre, sağlık harcamaları ile ekonomik büyüme arasında istatistikî olarak anlamlı bir ilişki tespit edilememiştir.In recent years, theoretical and empirical studies in the economic growth literature emphasize the role of human capital in the process of economic growth. In general, most of the empirical studies have centered on the relation between education and economic growth. In this study, the effect of health on economic growth has been tested by a panel data analysis. This study consists of annual data of 15 OECD countries for the period from 1990 to 2006. In the analyses, the share of public health expenditures in total health expenditures as well as other explanatory variables has been employed. The relationship between health expenditures and economic growth was estimated in Pooled Regression Model by the panel OLS method. As a result, we haven’t found any statistically significant relationship between health expenditures and economic growth

    Structural breaks, financial globalization, and financial development: Evidence from Turkey

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    Purpose ? Mishkin's hypothesis suggests that globalization appears to be a vital factor in stimulating the development of the financial system. The study examines this hypothesis for the Turkish economy from 1970 to 2017. It focuses on the link between financial globalization and financial development by integrating economic growth, inflation, and natural resource rent as additional determinants into the financial development specification. Methods ? The Ng-Perron and Vogelsang-Perron unit root tests are used to check the stationarity of variables. The cointegration analysis is performed using the Hatemi-J and ARDL bounds testing procedures.Findings ? The main empirical results show that the series are cointegrated under structural breaks; in the long run, financial globalization and economic growth increase financial development while inflation and natural resource rent negatively affect financial development. A unidirectional causality exists from financial globalization and economic growth to financial development. At the same time, there is bidirectional causality between inflation and financial development, natural resource rent, and financial development.Implications ? The empirical findings can present important recommendations for policymakers.Originality ? Very few time-series studies include Turkey's economy and structural breaks

    Akustik algılayıcı ağlarında çarpan çizgeleri kullanarak hedef konumlandırma = Target localization in acoustic sensor networks using factor graphs

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    We consider the problem of localizing targets which act as acoustic sources over a region covered by a sensor network in which each node is equipped with an acoustic intensity sensor. The a posteriori distribution of each target location is constructed through a message passing algorithm on the factor graph representation of the joint posterior which is based on the loopy sum product algorithm. After constructing the posteriors, it is possible to compute the MAP or MMSE estimation expressions of the target locations under these distributions. This approach is the application of an information architecture for distributed inference proposed before. Therefore it is naturally amenable to a distributed implementation and depending on functions which can be easily generated, it has the advantage of compliance with the requirements of sensor networks

    İletişim kısıtları altında dağıtık rasgele-alan kestirimi (Decentralized random-field estimation under communication constraints)

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    We consider the problem of decentralized estimation of a random-field under communication constraints in a Bayesian setting. The underlying system is composed of sensor nodes which collect measurements due to random variables they are associated with and which can communicate through finite-rate channels in accordance with a directed acyclic topology. After receiving the incoming messages if any, each node evaluates its local rule given its measurement and these messages, producing an estimate as well as outgoing messages to child nodes. A rigorous problem definition is achieved by constraining the feasible set through this structure in order to optimize a Bayesian risk function that captures the costs due to both communications and estimation errors. We adopt an iterative solution through a Team Decision Theoretic treatment previously proposed for decentralized detection. However, for the estimation problem, the iterations contain expressions with integral operators that have no closed form solutions in general. We propose approximations to these expressions through Monte Carlo methods. The result is an approximate computational scheme for optimization of distributed estimation networks under communication constraints. In an example scenario, we increase the price of communications and present the degrading estimation performance of the converged rules
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