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72,612
ON THE SOLUTIONS OF LINEAR ODD-ORDER HEAT-TYPE EQUATIONS WITH RANDOM INITIAL CONDITIONS
"... Abstract. In this paper odd-order heat-type equations with different random initial conditions are examined. In particular, we give rigorous conditions for the existence of the solutions in the case where the initial condition is represented by a strictly ϕ-subGaussian harmonized process η = η(x). ..."
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Abstract. In this paper odd-order heat-type equations with different random initial conditions are examined. In particular, we give rigorous conditions for the existence of the solutions in the case where the initial condition is represented by a strictly ϕ-subGaussian harmonized process η = η
CONSTRUCTING STATIONARY GAUSSIAN PROCESSES FROM DETERMINISTIC PROCESSES WITH RANDOM INITIAL CONDITIONS
, 2002
"... We consider a family of stationary Gaussian processes that includes the stationary Ornstein-Uhlenbeck process. We show that processes in this family can be attained as the limit of a sequence of deterministic processes with random initial conditions. Weak convergence in the supremum norm on finite t ..."
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Cited by 1 (1 self)
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We consider a family of stationary Gaussian processes that includes the stationary Ornstein-Uhlenbeck process. We show that processes in this family can be attained as the limit of a sequence of deterministic processes with random initial conditions. Weak convergence in the supremum norm on finite
Shallow Parsing with Conditional Random Fields
, 2003
"... Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position. Among sequence labeling tasks in language processing, shallow parsing has received much attention, with the development of standard evaluati ..."
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Cited by 581 (8 self)
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Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position. Among sequence labeling tasks in language processing, shallow parsing has received much attention, with the development of standard
Initial Conditions and Moment Restrictions in Dynamic Panel Data Models
- Journal of Econometrics
, 1998
"... Estimation of the dynamic error components model is considered using two alternative linear estimators that are designed to improve the properties of the standard firstdifferenced GMM estimator. Both estimators require restrictions on the initial conditions process. Asymptotic efficiency comparisons ..."
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Cited by 2393 (16 self)
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Estimation of the dynamic error components model is considered using two alternative linear estimators that are designed to improve the properties of the standard firstdifferenced GMM estimator. Both estimators require restrictions on the initial conditions process. Asymptotic efficiency
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
, 2001
"... We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions ..."
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Cited by 3485 (85 self)
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We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions
Randomized kinodynamic planning
- THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2001; 20; 378
, 2001
"... This paper presents the first randomized approach to kinodynamic planning (also known as trajectory planning or trajectory design). The task is to determine control inputs to drive a robot from an initial configuration and velocity to a goal configuration and velocity while obeying physically based ..."
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Cited by 626 (35 self)
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This paper presents the first randomized approach to kinodynamic planning (also known as trajectory planning or trajectory design). The task is to determine control inputs to drive a robot from an initial configuration and velocity to a goal configuration and velocity while obeying physically based
A randomized protocol for signing contracts
, 1990
"... Two parties, A and B, want to sign a contract C over a communication network. To do so, they must “simultaneously” exchange their commitments to C. Since simultaneous exchange is usually impossible in practice, protocols are needed to approximate simultaneity by exchanging partial commitments in pie ..."
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Cited by 599 (11 self)
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in piece by piece manner. During such a protocol, one party or another may have a slight advantage; a “fair” protocol keeps this advantage within acceptable limits. We present a new protocol that is fair in the sense that, at any stage in its execution, the conditional probability that one party cannot
CONDENSATION -- conditional density propagation for visual tracking
, 1998
"... The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses “factored sampling”, previously applied to th ..."
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Cited by 1503 (12 self)
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to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Condensation uses learned dynamical models, together with visual observations, to propagate the random set over time. The result is highly robust tracking of agile motion
Results 1 - 10
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72,612
