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In this paper, the empirical Bayes two-sided test problem of the location parameter for lognormal distribution is investigated. By using the kernel-type density estimation, the empirical Bayes two-sided test rule is constructed. It is shown that the proposed empirical Bayes test rule is asymptotically optimal and its convergence rate is obtained under suitable conditions.
Awerbuch and Scheideler have shown that peer-to-peer overlays networks can survive Byzantine attacks only if malicious nodes are not able to predict what will be the topology of the network for a given sequence of join and leave operations. In this paper we investigate adversarial strategies by following specific protocols. Our analysis demonstrates first that an adversary can very quickly subvert...
A queueing network with negative customers (G-network) is considered with the Poisson flow of positive customers, four types of nodes, and dependent service at different nodes. Every customer arriving at the network is determined by a set of random parameters: customer route, the length of customer route, customer size and its service time at each route stage as well. The arrival of a negative customer...
A macro-action is a typical series of useful actions that brings high expected rewards to an agent. Murata et al. have proposed an actor-critic model which can generate macro-actions automatically based on the information on state values and visiting frequency of states. However, their model has not assumed that generated macro-actions are utilized for leaning different tasks. In this paper, we extend...
A longstanding problem in sequential Monte Carlo (SMC) is to mathematically prove the popular belief that resampling does improve the performance of the estimation (this of course is not always true, and the real question is to clarify classes of problems where resampling helps). A more pragmatic answer to the problem is to use adaptive procedures that have been proposed on the basis of heuristic...
Dynamical systems like neural networks based on lateral inhibition have a large field of applications in image processing, robotics and morphogenesis modelling. In this paper, we deal with a double approach, image processing and neural networks modelling both based on lateral inhibition in Markov random field to understand a degenerative disease, the retinitis pigmentosa.
The problem of hypothesis testing against independence for a Gauss-Markov random field (GMRF) is analyzed. Assuming an acyclic dependency graph, an expression for the log-likelihood ratio of detection is derived. Assuming random placement of nodes over a large region according to the Poisson or uniform distribution and nearest-neighbor dependency graph, the error exponent of the Neyman-Pearson detector...
This letter concerns with robust exponential stability of stochastic neural networks with Markovian switching. By applying Lyapunov functional method, a sufficient stabilization condition is developed in terms of matrix inequalities. The result is also compared with the previously reported results in the literature. Finally, one simulation example is provided to illustrate the effectiveness of the...
This paper presents the probabilistic logic model to compute the probability distribution of the nano gate states. The characterization is based on the Markov random field and statistic physics. The primary logic gates are probabilistically characterized. The effectiveness of the method is demonstrated by a full adder and an 8-bit adder. The analysis shows that the device probability distribution...
In this study hourly wind speed time series data of Eskisehir region, Turkey have been used for stochastic generation of wind speed data using the transition matrix approach of the Markov chain process. Previous work on synthetic data generation did not focus on the effects of different choices of wind states. In this work, it was observed that increasing the number of states has a significant benefit...
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