Results 1  10
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2,288
Estimating Wealth Effects without Expenditure Data— or Tears
 Policy Research Working Paper 1980, The World
, 1998
"... Abstract: We use the National Family Health Survey (NFHS) data collected in Indian states in 1992 and 1993 to estimate the relationship between household wealth and the probability a child (aged 6 to 14) is enrolled in school. A methodological difficulty to overcome is that the NFHS, modeled closely ..."
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Cited by 871 (16 self)
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closely on the Demographic and Health Surveys (DHS), measures neither household income nor consumption expenditures. As a proxy for longrun household wealth we construct a linear index from a set of asset indicators using principal components analysis to derive the weights. This “asset index ” is robust
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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with a single loop • Unless all the conditional probabilities are deter ministic, belief propagation will converge. • There is an analytic expression relating the cor rect marginals to the loopy marginals. The ap proximation error is related to the convergence rate of the messages the faster
MIMO Multiple Access Channel with an Arbitrarily Varying Eavesdropper: Secrecy degrees of freedom
 IEEE TRANSACTIONS ON INFORMATION THEORY, FEBRUARY
, 2013
"... A twotransmitter Gaussian multiple access wiretap channel with multiple antennas at each of the nodes is investigated. The channel matrices of the legitimate users are fixed and revealed to all the terminals, whereas the channel matrices of the eavesdropper are arbitrarily varying and only known t ..."
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Cited by 13 (5 self)
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. The converse involves establishing an upper bound on a weightedsumrate expression. This is accomplished by using induction, where at each step one combines the secrecy and multipleaccess constraints associated with an adversary eavesdropping a carefully selected group of subchannels.
Policy gradient methods for reinforcement learning with function approximation.
 In NIPS,
, 1999
"... Abstract Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. In this paper we explore an alternative approach in which the policy is explicitly repres ..."
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Cited by 439 (20 self)
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term reward obtained from it. We will give our results only once, but they will apply to this formulation as well under the definitions where γ ∈ [0, 1] is a discount rate (γ = 1 is allowed only in episodic tasks). In this formulation, we define d π (s) as a discounted weighting of states encountered starting
The Node Distribution of the Random Waypoint Mobility Model for Wireless Ad Hoc Networks
, 2003
"... The random waypoint model is a commonly used mobility model in the simulation of ad hoc networks. It is known that the spatial distribution of network nodes moving according to this model is, in general, nonuniform. However, a closedform expression of this distribution and an indepth investigation ..."
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Cited by 377 (10 self)
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of the resulting distribution is the weighted sum of three independent components: the static, pause, and mobility component. This division enables us to understand how the models parameters influence the distribution. We derive an exact equation of the asymptotically stationary distribution for movement on a
Linear Modeling Of mRNA Expression Levels During CNS Development And Injury
, 1999
"... this paper, we will leave out the inputs to these genes. Since the least squares solution essentially solves a linear regression for each gene independently, failure to achieve a biologically plausible model for some of the genes does not imply that the rest of the model is unreliable. The sum of in ..."
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Cited by 239 (3 self)
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of input weights to each gene is close to zero, i.e. there seem to be no genes that are primarily positively or negatively influenced by other genes. However, the sum of output weights from each gene varies more widely, with a standard deviation that is almost a magnitude larger than for the sum of input
Truthful Mechanisms for OneParameter Agents
"... In this paper, we show how to design truthful (dominant strategy) mechanisms for several combinatorial problems where each agent’s secret data is naturally expressed by a single positive real number. The goal of the mechanisms we consider is to allocate loads placed on the agents, and an agent’s sec ..."
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Cited by 232 (3 self)
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truthful mechanisms for maximum flow, Qjj P Cj (scheduling related machines to minimize the sum of completion times), optimizing an affine function over a fixed set, and special cases of uncapacitated facility location. In addition, for Qjj P wjCj (minimizing the weighted sum of completion times), we prove
Distributed regression: an efficient framework for modeling sensor network data
 In IPSN
, 2004
"... We present distributed regression, an efficient and general framework for innetwork modeling of sensor data. In this framework, the nodes of the sensor network collaborate to optimally fit a global function to each of their local measurements. The algorithm is based upon kernel linear regression, w ..."
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Cited by 178 (8 self)
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, where the model takes the form of a weighted sum of local basis functions; this provides an expressive yet tractable class of models for sensor network data. Rather than transmitting data to one another or outside the network, nodes communicate constraints on the model parameters, drastically reducing
Coil sensitivity encoding for fast MRI. In:
 Proceedings of the ISMRM 6th Annual Meeting,
, 1998
"... New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementa ..."
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Cited by 193 (3 self)
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contributions from a number of positions in the full FOV need to be separated. As depicted in The key to signal separation lies in the fact that in each singlecoil image signal superposition occurs with different weights according to local coil sensitivities. Consider one pixel in the reduced FOV
How often to sample a continuoustime process in the presence of market microstructure noise
 Review of Financial Studies
, 2005
"... In theory, the sum of squares of log returns sampled at high frequency estimates their variance. When market microstructure noise is present but unaccounted for, however, we show that the optimal sampling frequency is finite and derives its closedform expression. But even with optimal sampling, usi ..."
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Cited by 156 (14 self)
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In theory, the sum of squares of log returns sampled at high frequency estimates their variance. When market microstructure noise is present but unaccounted for, however, we show that the optimal sampling frequency is finite and derives its closedform expression. But even with optimal sampling
Results 1  10
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2,288