Results 1  10
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2,263
A model for technical inefficiency effects in a stochastic frontier production function for panel data
 Empirical Economics
, 1995
"... Abstract: A stochastic frontier production function is defined for panel data on firms, in which the nonnegative technical inetGciency effects are assumed to be a function of firmspecific variables and time. The inefficiency effects are assumed to be independently distributed as truncations of nor ..."
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Cited by 555 (4 self)
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of normal distributions with constant variance, but with means which are a linear function of observable variables. This panel data model is an extension of recently proposed models for inefTiciency effects in stochastic frontiers for crosssectional data. An empirical application of the model is obtained
Doubly stochastic normalization for spectral clustering
 In Proceedings of the conference on Neural Information Processing Systems (NIPS
, 2006
"... In this paper we focus on the issue of normalization of the affinity matrix in spectral clustering. We show that the difference between Ncuts and Ratiocuts is in the error measure being used (relativeentropy versus L1 norm) in finding the closest doublystochastic matrix to the input affinity mat ..."
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Cited by 16 (2 self)
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In this paper we focus on the issue of normalization of the affinity matrix in spectral clustering. We show that the difference between Ncuts and Ratiocuts is in the error measure being used (relativeentropy versus L1 norm) in finding the closest doublystochastic matrix to the input affinity
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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modification to the update rules in that we normalized both ..\ and 1r messages at each iteration. As Pearl Nodes were updated in parallel: at each iteration all nodes calculated their outgoing messages based on the incoming messages of their neighbors from the pre vious iteration. The messages were said
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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, but has several limitations. First, it is oriented toward finding deterministic policies, whereas the optimal policy is often stochastic, selecting different actions with specific probabilities (e.g., see In this paper we explore an alternative approach to function approximation in RL. Rather than
On the Estimation of Technical Inefficiency in the Stochastic Frontier Production Function Model
 Journal of Econometrics
, 1982
"... The error term in the stochastic frontier model is of the form (CU), where u is a normal error term representing pure randomness, and u is a nonnegative error term representing technical inefficiency. The entire (UU) is easily estimated for each observation, but a previously unsolved problem is h ..."
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Cited by 343 (5 self)
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The error term in the stochastic frontier model is of the form (CU), where u is a normal error term representing pure randomness, and u is a nonnegative error term representing technical inefficiency. The entire (UU) is easily estimated for each observation, but a previously unsolved problem
The minimum description length principle in coding and modeling
 IEEE TRANS. INFORM. THEORY
, 1998
"... We review the principles of Minimum Description Length and Stochastic Complexity as used in data compression and statistical modeling. Stochastic complexity is formulated as the solution to optimum universal coding problems extending Shannon’s basic source coding theorem. The normalized maximized ..."
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Cited by 394 (18 self)
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We review the principles of Minimum Description Length and Stochastic Complexity as used in data compression and statistical modeling. Stochastic complexity is formulated as the solution to optimum universal coding problems extending Shannon’s basic source coding theorem. The normalized maximized
Correlation And Dependence In Risk Management: Properties And Pitfalls
 RISK MANAGEMENT: VALUE AT RISK AND BEYOND
, 1999
"... Modern risk management calls for an understanding of stochastic dependence going beyond simple linear correlation. This paper deals with the static (nontimedependent) case and emphasizes the copula representation of dependence for a random vector. Linear correlation is a natural dependence measure ..."
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Cited by 338 (39 self)
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Modern risk management calls for an understanding of stochastic dependence going beyond simple linear correlation. This paper deals with the static (nontimedependent) case and emphasizes the copula representation of dependence for a random vector. Linear correlation is a natural dependence
Asymptotic error distributions for the Euler method for stochastic differential equations
 THE ANNALS OF PROBABILITY
, 1998
"... We are interested in the rate of convergence of the Euler scheme approximation of the solution to a stochastic differential equation driven by a general (possibly discontinuous) semimartingale, and by the asymptotic behavior of the associated normalized error. It is well known that for Itô’s equatio ..."
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Cited by 176 (13 self)
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We are interested in the rate of convergence of the Euler scheme approximation of the solution to a stochastic differential equation driven by a general (possibly discontinuous) semimartingale, and by the asymptotic behavior of the associated normalized error. It is well known that for Itô’s
Dynamic Programming for Partially Observable Stochastic Games
 IN PROCEEDINGS OF THE NINETEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 2004
"... We develop an exact dynamic programming algorithm for partially observable stochastic games (POSGs). The algorithm is a synthesis of dynamic programming for partially observable Markov decision processes (POMDPs) and iterated elimination of dominated strategies in normal form games. ..."
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Cited by 159 (25 self)
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We develop an exact dynamic programming algorithm for partially observable stochastic games (POSGs). The algorithm is a synthesis of dynamic programming for partially observable Markov decision processes (POMDPs) and iterated elimination of dominated strategies in normal form games.
Results 1  10
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2,263