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Optimal sequential procedures with Bayes decision rules

Andrey Novikov (2010)

Kybernetika

In this article, a general problem of sequential statistical inference for general discrete-time stochastic processes is considered. The problem is to minimize an average sample number given that Bayesian risk due to incorrect decision does not exceed some given bound. We characterize the form of optimal sequential stopping rules in this problem. In particular, we have a characterization of the form of optimal sequential decision procedures when the Bayesian risk includes both the loss due to incorrect...

Parametric inference for mixed models defined by stochastic differential equations

Sophie Donnet, Adeline Samson (2008)

ESAIM: Probability and Statistics

Non-linear mixed models defined by stochastic differential equations (SDEs) are considered: the parameters of the diffusion process are random variables and vary among the individuals. A maximum likelihood estimation method based on the Stochastic Approximation EM algorithm, is proposed. This estimation method uses the Euler-Maruyama approximation of the diffusion, achieved using latent auxiliary data introduced to complete the diffusion process between each pair of measurement instants. A tuned...

Parametrization and geometric analysis of coordination controllers for multi-agent systems

Xiaoli Wang, Yiguang Hong (2009)

Kybernetika

In this paper, we address distributed control structures for multi-agent systems with linear controlled agent dynamics. We consider the parametrization and related geometric structures of the coordination controllers for multi-agent systems with fixed topologies. Necessary and sufficient conditions to characterize stabilizing consensus controllers are obtained. Then we consider the consensus for the multi-agent systems with switching interaction topologies based on control parametrization.

Particle approximations of Lyapunov exponents connected to Schrödinger operators and Feynman–Kac semigroups

Pierre Del Moral, L. Miclo (2003)

ESAIM: Probability and Statistics

We present an interacting particle system methodology for the numerical solving of the Lyapunov exponent of Feynman–Kac semigroups and for estimating the principal eigenvalue of Schrödinger generators. The continuous or discrete time models studied in this work consists of N interacting particles evolving in an environment with soft obstacles related to a potential function V . These models are related to genetic algorithms and Moran type particle schemes. Their choice is not unique. We will examine...

Particle approximations of Lyapunov exponents connected to Schrödinger operators and Feynman–Kac semigroups

Pierre Del Moral, L. Miclo (2010)

ESAIM: Probability and Statistics

We present an interacting particle system methodology for the numerical solving of the Lyapunov exponent of Feynman–Kac semigroups and for estimating the principal eigenvalue of Schrödinger generators. The continuous or discrete time models studied in this work consists of N interacting particles evolving in an environment with soft obstacles related to a potential function V. These models are related to genetic algorithms and Moran type particle schemes. Their choice is not unique. We...

Plan de muestreo secuencial basado en la energía informacional para una población exponencial.

Leandro Pardo, Domingo Morales, Vicente Quesada (1985)

Trabajos de Estadística e Investigación Operativa

En Pardo (1984), se propuso un Plan de Muestreo Secuencial basado en la Energía Informacional (P.M.S.E.I.), análogo al propuesto por Lindley (1956, 1957) a partir de la Entropía de Shannon, con el fin de recoger información acerca de un parámetro desconocido θ. En esta comunicación se aplica el P.M.S.E.I. al caso concreto de la recogida de información acerca del parámetro θ de una distribución exponencial y se extiende el concepto de P.M.S.E.I. al caso en que el estadístico esté interesado en recoger...

Currently displaying 141 – 160 of 246