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Local asymptotic normality for finite . . .
, 2008
"... The previous results on local asymptotic normality (LAN) for qubits [20, 17] are extended to quantum systems of arbitrary finite dimension d. LAN means that the quantum statistical model consisting of n identically prepared ddimensional systems with joint state ρ⊗n converges as n → ∞ to a statisti ..."
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The previous results on local asymptotic normality (LAN) for qubits [20, 17] are extended to quantum systems of arbitrary finite dimension d. LAN means that the quantum statistical model consisting of n identically prepared ddimensional systems with joint state ρ⊗n converges as n → ∞ to a
Local asymptotic normality in quantum statistics
, 2006
"... The theory of local asymptotic normality for quantum statistical experiments is developed in the spirit of the classical result from mathematical statistics due to Le Cam. Roughly speaking, local asymptotic normality means that the family ϕn θ0+u / √ n consisting of joint states of n identically pr ..."
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Cited by 18 (2 self)
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The theory of local asymptotic normality for quantum statistical experiments is developed in the spirit of the classical result from mathematical statistics due to Le Cam. Roughly speaking, local asymptotic normality means that the family ϕn θ0+u / √ n consisting of joint states of n identically
Local asymptotic normality for normal inverse Gaussian Lévy processes with highfrequency sampling
 Markov processes, semigroups and generators, volume 38 of de Gruyter Studies in Mathematics. Walter de Gruyter
, 2011
"... Local asymptotic normality for ..."
On Local Asymptotic Normality for Birth and Death on a Flow
"... We consider statistical models for birth and death on a flow and prove local asymptotic normality as the observation time approaches infinity; as a consequence, we know how to characterize asymptotically efficient estimators for the unknown parameter. We construct a sequence of minimum distance est ..."
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We consider statistical models for birth and death on a flow and prove local asymptotic normality as the observation time approaches infinity; as a consequence, we know how to characterize asymptotically efficient estimators for the unknown parameter. We construct a sequence of minimum distance
Fisher informations and local asymptotic normality for continuoustime quantum Markov processes
"... We consider the problem of estimating an arbitrary dynamical parameter of an quantum open system in the inputoutput formalism. For irreducible Markov processes, we show that in the limit of large times the systemoutput state can be approximated by a quantum Gaussian state whose mean is proportiona ..."
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and homodyne measurements also satisfy local asymptotic normality and we compute the corresponding classical Fisher informations. The mathematical theorems are illustrated with the examples of a twolevel system and the atom maser. Our results contribute towards a better understanding of the statistical
EQUIVALENCE CLASSES AND LOCAL ASYMPTOTIC NORMALITY IN SYSTEM IDENTIFICATION FOR QUANTUM MARKOV CHAINS
"... Abstract. We consider the problems of identifying and estimating dynamical parameters of an ergodic quantum Markov chain, when only the stationary output is accessible for measurements. On the identifiability question, we show that the knowledge of the output state completely fixes the dynamics up t ..."
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Cited by 1 (0 self)
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) quantum Fisher information of the output, which is proportional to the Markov variance of a certain ‘generator’. More generally, we show that the output is locally asymptotically normal, i.e. it can be approximated by a simple quantum Gaussian model consisting of a coherent state whose mean is related
2001a). Diffusions with measurement errors. I — local asymptotic normality. ESAIM: Probability and Statistics
"... Abstract. We consider a diusion process X which is observed at times i=n for i = 0; 1; : : : ; n, each observation being subject to a measurement error. All errors are independent and centered Gaussian with known variance n. There is an unknown parameter within the diusion coecient, to be estimated. ..."
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Cited by 36 (3 self)
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Abstract. We consider a diusion process X which is observed at times i=n for i = 0; 1; : : : ; n, each observation being subject to a measurement error. All errors are independent and centered Gaussian with known variance n. There is an unknown parameter within the diusion coecient, to be estimated. In this rst paper the case when X is indeed a Gaussian martingale is examined: we can prove that the LAN property holds under quite weak smoothness assumptions, with an explicit limiting Fisher information. What is perhaps the most interesting is the rate at which this convergence takes place: it is 1= p n (as when there is no measurement error) when n goes fast enough to 0, namely nn is bounded. Otherwise, and provided the sequence n itself is bounded, the rate is (n=n) 1=4. In particular if n = does not depend on n, we get a rate n
Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 594 (53 self)
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This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias
Results 1  10
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2,378,703