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ON THE ERROR EXPONENTS FOR DETECTING RANDOMLY SAMPLED NOISY DIFFUSION PROCESSES
"... This paper deals with the detection of a continuous random process described by an OrnsteinUhlenbeck (OU) stochastic differential equation. Randomly spaced sensors or equivalently a random time sampler which deliver noisy samples of the process are used for this detection. Two types of tests are c ..."
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This paper deals with the detection of a continuous random process described by an OrnsteinUhlenbeck (OU) stochastic differential equation. Randomly spaced sensors or equivalently a random time sampler which deliver noisy samples of the process are used for this detection. Two types of tests
Error exponents for NeymanPearson detection of a continuoustime Gaussian Markov process from noisy irregular samples
, 2009
"... This paper addresses the detection of a stochastic process in noise from irregular samples. We consider two hypotheses. The noise only hypothesis amounts to model the observations as a sample of a i.i.d. Gaussian random variables (noise only). The signal plus noise hypothesis models the observations ..."
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Cited by 5 (1 self)
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This paper addresses the detection of a stochastic process in noise from irregular samples. We consider two hypotheses. The noise only hypothesis amounts to model the observations as a sample of a i.i.d. Gaussian random variables (noise only). The signal plus noise hypothesis models
Error exponents for bayesian detection with randomly spaced sensors
 IEEE Tran. on Signal Processing
"... We study the detection of GaussMarkov signals using randomly spaced sensors. We derive a lower bound on the Bayesian detection error based on the KullbackLeibler divergence, and from this, define an error exponent. We then evaluate the error exponent for stationary and nonstationary GaussMarkov ..."
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Cited by 4 (2 self)
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We study the detection of GaussMarkov signals using randomly spaced sensors. We derive a lower bound on the Bayesian detection error based on the KullbackLeibler divergence, and from this, define an error exponent. We then evaluate the error exponent for stationary and nonstationary Gauss
Error Exponents for the Detection of Gauss–Markov Signals Using Randomly Spaced Sensors
"... Abstract—We derive the Neyman–Pearson error exponent for the detection of Gauss–Markov signals using randomly spaced sensors. We assume that the sensor spacings, I P FFF are drawn independently from a common density @ A, and we treat both stationary and nonstationary Markov models. Error exponents a ..."
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Abstract—We derive the Neyman–Pearson error exponent for the detection of Gauss–Markov signals using randomly spaced sensors. We assume that the sensor spacings, I P FFF are drawn independently from a common density @ A, and we treat both stationary and nonstationary Markov models. Error exponents
Detection Error Exponent for Spatially Dependent Samples in Random Networks
"... Abstract—The problem of binary hypothesis testing is considered when the measurements are drawn from a Markov random field (MRF) under each hypothesis. Spatial dependence of the measurements is incorporated by parameterizing the clique potential functions of each MRF with the location of the nodes f ..."
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from which the samples are collected. The nodes are placed i.i.d. in expanding areas with increasing sample size. Asymptotic performance of hypothesis testing is analyzed through the NeymanPearson error exponent. It is shown that the exponent reduces to a functional on a stabilizing graph. Using
Semantic cognition: A parallel distributed processing approach
 Connectionist perspectives on categoryspecific deficits. In: Categoryspecificity in brain and
, 2004
"... Copyright c ..."
Intertemporal Substitution In Labor Supply: Evidence From Micro Data
 Journal of Political Economy
, 1986
"... The sensitivity of the supply of labor to intertemporal variation in the wage is an important issue in macroeconomics, the analysis of social security and pensions, and the study of life cycle patterns of work. This paper explores two approaches to the measurement of intertemporal substi tution ..."
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Cited by 219 (3 self)
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The sensitivity of the supply of labor to intertemporal variation in the wage is an important issue in macroeconomics, the analysis of social security and pensions, and the study of life cycle patterns of work. This paper explores two approaches to the measurement of intertemporal substi tution which have appeared in the literature. The first approach is to use consumption to control for wealth and unobserved expectations about future wages in the labor supply equation. The second approach is to estimate a first difference equation for hours in which labor supply from the previous period serves as a control for wealth and wage expectations. The results indicate that the intertemporal substitution elasticity for married men is positive but small. 1.
ESTIMATING EDDY DIFFUSIVITIES FROM NOISY LAGRANGIAN OBSERVATIONS
, 2009
"... The problem of estimating the eddy diffusivity from Lagrangian observations in the presence of measurement error is studied in this paper. We consider a class of incompressible velocity fields for which is can be rigorously proved that the small scale dynamics can be parameterised in terms of an e ..."
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Cited by 3 (0 self)
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The problem of estimating the eddy diffusivity from Lagrangian observations in the presence of measurement error is studied in this paper. We consider a class of incompressible velocity fields for which is can be rigorously proved that the small scale dynamics can be parameterised in terms
Detecting and estimating signals in noisy cable structures: II. Information theoretical analysis
, 1999
"... This is the second in a series of papers which attempt to recast classical singleneuron biophysics in information theoretical terms. Classical cable theory focuses on analyzing the voltage or current attenuation of a synaptic signal as it propagates from its dendritic input location to the spike in ..."
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Cited by 54 (6 self)
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initiation zone. On the other hand, we are interested in analyzing the amount of information lost about the signal in this process due to the presence of various noise sources distributed throughout the neuronal membrane. We use a stochastic version of the linear onedimensional cable equation to derive
Results 1  10
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205,217