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9,139
Random Early Detection Gateways for Congestion Avoidance.
 IEEELACM Transactions on Networking,
, 1993
"... AbstractThis paper presents Random Early Detection (RED) gateways for congestion avoidance in packetswitched networks. The gateway detects incipient congestion by computing the average queue size. The gateway could notify connections of congestion either by dropping packets arriving at the gatewa ..."
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Cited by 2716 (31 self)
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at the gateway or by setting a bit in packet headers. When the average queue size exceeds a preset threshold, the gateway drops or marks each arriving packet with a certain probability, where the exact probability is a function of the average queue size. RED gateways keep the average queue size low while
Functional siRNAs and miRNAs exhibit strand bias
 Cell
, 2003
"... regulate mRNA levels through a cleavage event, miRNAs ..."
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Cited by 357 (1 self)
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regulate mRNA levels through a cleavage event, miRNAs
Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
, 2000
"... We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is independent of the potential outcomes given pretreatment variables, biases associated with simple treatmentcontrol average comparisons can be removed by adjusting for diff ..."
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Cited by 416 (35 self)
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for differences in the pretreatmentvariables. Rosenbaum and Rubin (1983, 1984) show that adjusting solely for differences between treated and control units in a scalar function of the pretreatment variables, the propensity score, also removes the entire bias associated with differences in pre
The Determinants of Credit Spread Changes.
 Journal of Finance
, 2001
"... ABSTRACT Using dealer's quotes and transactions prices on straight industrial bonds, we investigate the determinants of credit spread changes. Variables that should in theory determine credit spread changes have rather limited explanatory power. Further, the residuals from this regression are ..."
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Cited by 422 (2 self)
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the structural framework, default is triggered when the leverage ratio approaches unity. Hence, it is clear that credit spreads are expected to increase with leverage. Likewise, credit spreads should be a decreasing function of the firm's return on equity, all else equal. Changes in Volatility
Learning from demonstration”.
 Advances in Neural Information Processing Systems 9.
, 1997
"... Abstract By now it is widely accepted that learning a task from scratch, i.e., without any prior knowledge, is a daunting undertaking. Humans, however, rarely attempt to learn from scratch. They extract initial biases as well as strategies how to approach a learning problem from instructions and/or ..."
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Cited by 399 (32 self)
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Abstract By now it is widely accepted that learning a task from scratch, i.e., without any prior knowledge, is a daunting undertaking. Humans, however, rarely attempt to learn from scratch. They extract initial biases as well as strategies how to approach a learning problem from instructions and
Measuring the Nonlinear Biasing Function from a Galaxy Redshift Survey
, 2000
"... We present a simple method for evaluating the nonlinear biasing function of galaxies from a redshift survey. The nonlinear biasing is characterized by the conditional mean of the galaxy density fluctuation given the underlying mass density fluctuation 〈δgδ〉, or by the associated parameters of mean ..."
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We present a simple method for evaluating the nonlinear biasing function of galaxies from a redshift survey. The nonlinear biasing is characterized by the conditional mean of the galaxy density fluctuation given the underlying mass density fluctuation 〈δgδ〉, or by the associated parameters of mean
SmallBias Probability Spaces: Efficient Constructions and Applications
 SIAM J. Comput
, 1993
"... We show how to efficiently construct a small probability space on n binary random variables such that for every subset, its parity is either zero or one with "almost" equal probability. They are called fflbiased random variables. The number of random bits needed to generate the random var ..."
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Cited by 276 (13 self)
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variables is O(log n + log 1 ffl ). Thus, if ffl is polynomially small, then the size of the sample space is also polynomial. Random variables that are fflbiased can be used to construct "almost" kwise independent random variables where ffl is a function of k. These probability spaces have
A lower bound on the Bayesian MSE based on the optimal bias function
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2009
"... A lower bound on the minimum meansquared error (MSE) in a Bayesian estimation problem is proposed in this paper. This bound utilizes a wellknown connection to the deterministic estimation setting. Using the prior distribution, the bias function which minimizes the Cramér–Rao bound can be determin ..."
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Cited by 6 (0 self)
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A lower bound on the minimum meansquared error (MSE) in a Bayesian estimation problem is proposed in this paper. This bound utilizes a wellknown connection to the deterministic estimation setting. Using the prior distribution, the bias function which minimizes the Cramér–Rao bound can
On Recovering the Nonlinear Bias Function from Counts in Cells Measurements
, 2003
"... We present a simple and accurate method to constrain galaxy bias based on the distribution of counts in cells. The most unique feature of our technique is that it is applicable to nonlinear scales, where both dark matter statistics and the nature of galaxy bias are fairly complex. First, we estimat ..."
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Cited by 1 (1 self)
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estimate the underlying continuous distribution function from precise countsincells measurements assuming local Poisson sampling. Then a robust, nonparametric inversion of the bias function is recovered from the comparison of the cumulative distributions in simulated dark matter and galaxy catalogs
1 A Lower Bound on the Bayesian MSE Based on the Optimal Bias Function
, 804
"... A lower bound on the minimum meansquared error (MSE) in a Bayesian estimation problem is proposed in this paper. The bound is based on the approach of Young and Westerberg, in which a connection to the deterministic estimation setting is utilized. Using the prior distribution, the bias function whi ..."
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A lower bound on the minimum meansquared error (MSE) in a Bayesian estimation problem is proposed in this paper. The bound is based on the approach of Young and Westerberg, in which a connection to the deterministic estimation setting is utilized. Using the prior distribution, the bias function
Results 11  20
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9,139