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GLOBAL SUPPORT PROPERTIES OF STATIONARY ERGODIC PROCESSES
"... Nelson [7] has made the deep observation that a variety of quantum fields analytically continued to imaginary time are represented by stationary, ergodic, generalized Markov processes (see also [12, 14]). Recently, there has been some interest in determining the support properties of the measure in ..."
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Nelson [7] has made the deep observation that a variety of quantum fields analytically continued to imaginary time are represented by stationary, ergodic, generalized Markov processes (see also [12, 14]). Recently, there has been some interest in determining the support properties of the measure
Predictive complexity and generalized entropy rate of stationary ergodic processes
 In Algorithmic Learning Theory
, 2012
"... ar ..."
Good ErrorCorrecting Codes based on Very Sparse Matrices
, 1999
"... We study two families of errorcorrecting codes defined in terms of very sparse matrices. "MN" (MacKayNeal) codes are recently invented, and "Gallager codes" were first investigated in 1962, but appear to have been largely forgotten, in spite of their excellent properties. The ..."
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Cited by 750 (23 self)
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but also for any channel with symmetric stationary ergodic noise. We give experimental results for binarysymmetric channels and Gaussian channels demonstrating that practical performance substantially better than that of standard convolutional and concatenated codes can be achieved; indeed
Exact Sampling with Coupled Markov Chains and Applications to Statistical Mechanics
, 1996
"... For many applications it is useful to sample from a finite set of objects in accordance with some particular distribution. One approach is to run an ergodic (i.e., irreducible aperiodic) Markov chain whose stationary distribution is the desired distribution on this set; after the Markov chain has ..."
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Cited by 543 (13 self)
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For many applications it is useful to sample from a finite set of objects in accordance with some particular distribution. One approach is to run an ergodic (i.e., irreducible aperiodic) Markov chain whose stationary distribution is the desired distribution on this set; after the Markov chain
Testing for Common Trends
 Journal of the American Statistical Association
, 1988
"... Cointegrated multiple time series share at least one common trend. Two tests are developed for the number of common stochastic trends (i.e., for the order of cointegration) in a multiple time series with and without drift. Both tests involve the roots of the ordinary least squares coefficient matrix ..."
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Cited by 464 (7 self)
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has k unit roots and n k distinct stationary linear combinations. Our proposed tests can be viewed alternatively as tests of the number of common trends, linearly independent cointegrating vectors, or autoregressive unit roots of the vector process. Both of the proposed tests are asymptotically
Practical animation of liquids
 Graphical Models and Image Processing
, 1996
"... We present a comprehensive methodology for realistically animating liquid phenomena. Our approach unifies existing computer graphics techniques for simulating fluids and extends them by incorporating more complex behavior. It is based on the NavierStokes equations which couple momentum and mass con ..."
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Cited by 445 (26 self)
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the position of spray and foam during the animation process. Typical disadvantages to dynamic simulations such as poor scalability and lack of control are addressed by assuming that stationary obstacles align with grid cells during the finite difference discretization, and by appending terms to the Navier
On hypotheses testing for ergodic processes
 In Proceedgings of Information Theory Workshop (2008
, 1998
"... We propose a method for statistical analysis of time series, that allows us to obtain solutions to some classical problems of mathematical statistics under the only assumption that the process generating the data is stationary ergodic. Namely, we consider three problems: goodnessoffit (or identity ..."
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Cited by 18 (18 self)
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We propose a method for statistical analysis of time series, that allows us to obtain solutions to some classical problems of mathematical statistics under the only assumption that the process generating the data is stationary ergodic. Namely, we consider three problems: goodnessoffit (or
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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;actorcritic" or policyiteration architectures (e.g., Policy Gradient Theorem We consider the standard reinforcement learning framework (see, e.g., Sutton and Barto, 1998), in which a learning agent interacts with a Markov decision process (MDP). The state, action, and reward at each time t ∈ {0, 1, 2
1 Nonparametric Statistical Inference for Ergodic Processes
"... Abstract—In this work a method for statistical analysis of time series is proposed, which is used to obtain solutions to some classical problems of mathematical statistics under the only assumption that the process generating the data is stationary ergodic. Namely, three problems are considered: goo ..."
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Cited by 25 (22 self)
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Abstract—In this work a method for statistical analysis of time series is proposed, which is used to obtain solutions to some classical problems of mathematical statistics under the only assumption that the process generating the data is stationary ergodic. Namely, three problems are considered
Asymptotic Recurrence And Waiting Times For Stationary Processes
 J. Theoret. Probab
, 1998
"... this paper we investigate the asymptotic behavior of recurrence and waiting times for finitevalued stationary processes, under various mixing conditions. Let X = fX n ; n 2 Zg be a stationary ergodic process on the space of infinite sequences (S ..."
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Cited by 39 (11 self)
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this paper we investigate the asymptotic behavior of recurrence and waiting times for finitevalued stationary processes, under various mixing conditions. Let X = fX n ; n 2 Zg be a stationary ergodic process on the space of infinite sequences (S
Results 1  10
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