Results 1  10
of
3,635
On the parametric approximation in quantum optics
, 1999
"... Summary. — We perform the exact numerical diagonalization of the Hamiltonians that describe both degenerate and nondegenerate parametric amplifiers, by exploiting the conservation laws pertaining each device. We clarify the conditions under which the parametric approximation holds, showing that the ..."
Abstract
 Add to MetaCart
Summary. — We perform the exact numerical diagonalization of the Hamiltonians that describe both degenerate and nondegenerate parametric amplifiers, by exploiting the conservation laws pertaining each device. We clarify the conditions under which the parametric approximation holds, showing
NARX Models: Optimal Parametric Approximation of
"... Abst ract we have that Bayesian regression, a nonparametric identification technique with several appealing features, can be applied to the identification of NARX (nonlinear ARX) models. However, its computational complexity scales as O(N 3) where N is the data set size. In order to reduce complex ..."
Abstract
 Add to MetaCart
duce complexity, the challenge is to obtain fixedorder parametric models capable of approximating accurately the nonparametric Bayes estimate avoiding its explicit computation. In this work we derive, optimal finitedimensional approximations of complexity O(N 2) focusing on their use in the parametric
nonparametric approximation of several samples
, 2006
"... Quantifying the cost of simultaneous ..."
Nonparametric Approximate Linear Programming for MDPs
"... The Approximate Linear Programming (ALP) approach to value function approximation for MDPs is a parametric value function approximation method, in that it represents the value function as a linear combination of features which are chosen a priori. Choosing these features can be a difficult challenge ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
The Approximate Linear Programming (ALP) approach to value function approximation for MDPs is a parametric value function approximation method, in that it represents the value function as a linear combination of features which are chosen a priori. Choosing these features can be a difficult
NARX Models: Optimal Parametric Approximation of Nonparametric Estimators
"... Bayesian regression, a nonparametric identification technique with several appealing features, can be applied to the identification of NARX (nonlinear ARX) models. However, its computational complexity scales as O(N3) where N is the data set size. In order to reduce complexity, the challenge is to ..."
Abstract
 Add to MetaCart
is to obtain fixedorder parametric models capable of approximating accurately the nonparametric Bayes estimate avoiding its explicit computation. In this work we derive, optimal finitedimensional approximations of complexity O(N2) focusing on their use in the parametric identification of NARX models. Key
THE KNOWLEDGE GRADIENT ALGORITHM USING LOCALLY PARAMETRIC APPROXIMATIONS
"... We are interested in maximizing a general (but continuous) function where observations are noisy and may be expensive. We derive a knowledge gradient policy, which chooses measurements which maximize the expected value of information, while using a locally parametric belief model which uses linear a ..."
Abstract
 Add to MetaCart
We are interested in maximizing a general (but continuous) function where observations are noisy and may be expensive. We derive a knowledge gradient policy, which chooses measurements which maximize the expected value of information, while using a locally parametric belief model which uses linear
Parametric Approximation Algorithms for HighDimensional Euclidean Similarity
, 2001
"... . We introduce a spectrum of algorithms for measuring the ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
. We introduce a spectrum of algorithms for measuring the
Nonparametric approximate dynamic programming via the kernel method.
 In Advances in Neural Information Processing Systems,
, 2012
"... Abstract This paper presents a novel, nonparametric approximate dynamic programming (ADP) algorithm that enjoys dimensionindependent approximation and sample complexity guarantees. We obtain this algorithm by 'kernelizing' a recent mathematical program for ADP (the 'smoothed approx ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
Abstract This paper presents a novel, nonparametric approximate dynamic programming (ADP) algorithm that enjoys dimensionindependent approximation and sample complexity guarantees. We obtain this algorithm by 'kernelizing' a recent mathematical program for ADP (the &apos
How much should we trust differencesindifferences estimates?
, 2003
"... Most papers that employ DifferencesinDifferences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent. To illustrate the severity of this issue, we randomly generate placebo laws in statelevel data on femal ..."
Abstract

Cited by 828 (1 self)
 Add to MetaCart
at the 5 percent level for up to 45 percent of the placebo interventions. We use Monte Carlo simulations to investigate how well existing methods help solve this problem. Econometric corrections that place a specific parametric form on the timeseries process do not perform well. Bootstrap (taking
Results 1  10
of
3,635