Displaying similar documents to “Estimation of parameters in a network reliability model with spatial dependence”

Estimation of parameters in a network reliability model with spatial dependence

Ian Hepburn Dinwoodie (2005)

ESAIM: Probability and Statistics

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An iterative method based on a fixed-point property is proposed for finding maximum likelihood estimators for parameters in a model of network reliability with spatial dependence. The method is shown to converge at a geometric rate under natural conditions on data.

Evolutionary learning of rich neural networks in the Bayesian model selection framework

Matteo Matteucci, Dario Spadoni (2004)

International Journal of Applied Mathematics and Computer Science

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In this paper we focus on the problem of using a genetic algorithm for model selection within a Bayesian framework. We propose to reduce the model selection problem to a search problem solved using evolutionary computation to explore a posterior distribution over the model space. As a case study, we introduce ELeaRNT (Evolutionary Learning of Rich Neural Network Topologies), a genetic algorithm which evolves a particular class of models, namely, Rich Neural Networks (RNN), in order to...

About the maximum information and maximum likelihood principles

Igor Vajda, Jiří Grim (1998)

Kybernetika

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Neural networks with radial basis functions are considered, and the Shannon information in their output concerning input. The role of information- preserving input transformations is discussed when the network is specified by the maximum information principle and by the maximum likelihood principle. A transformation is found which simplifies the input structure in the sense that it minimizes the entropy in the class of all information-preserving transformations. Such transformation need...

Inverse problem for networks of laser interferometers

Piotr Jaranowski (1997)

Banach Center Publications

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Estimation of the parameters of the gravitational-wave signal from a coalescing binary by a network of laser interferometers is considered. A generalization of the solution of the inverse problem found previously for the network of 3 detectors to the network of N detectors is given. Maximum likelihood and least squares estimators are applied to obtain the solution. Accuracy of the estimation of the parameters is assessed from the inverse of the Fisher information matrix. The results...

Mixture of experts architectures for neural networks as a special case of conditional expectation formula

Jiří Grim (1998)

Kybernetika

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Recently a new interesting architecture of neural networks called “mixture of experts” has been proposed as a tool of real multivariate approximation or prediction. We show that the underlying problem is closely related to approximating the joint probability density of involved variables by finite mixture. Particularly, assuming normal mixtures, we can explicitly write the conditional expectation formula which can be interpreted as a mixture-of- experts network. In this way the related...