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909
GENERATING MARKOV EVOLUTIONARY MATRICES FOR A GIVEN BRANCH LENGTH
"... Abstract. Under a markovian evolutionary process, the expected number of substitutions per site (branch length) that occur when a sequence evolves from another via a transition matrix P can be approximated by −1/4 log(det P). In continuoustime models, it is easy to simulate the process for any give ..."
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given branch length. For discretetime models, it is not so trivial. In this paper we solve this problem for the most wellknown discretetime models JC69 ∗ , K80 ∗ , K81 ∗ , SSM, and GMM and we provide concise algorithms to generate stochastic matrices of given determinant. These models have
Dynamic Programming for DiscreteTime
"... We generalise the optimisation technique of dynamic programming for discretetime systems with an uncertain gain function. We assume that uncertainty about the gain function is described by an imprecise probability model, which generalises the wellknown Bayesian, or precise, models. We compare va ..."
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We generalise the optimisation technique of dynamic programming for discretetime systems with an uncertain gain function. We assume that uncertainty about the gain function is described by an imprecise probability model, which generalises the wellknown Bayesian, or precise, models. We compare
A DiscreteTime Model for Multimedia Correlated Sources
, 1995
"... this paper a multimedia source model is presented. In order to capture the intermedia synchronization requirements of the streams in the multimedia flow, the model is defined as the superposition of heterogeneous correlated monomedia arrival processes. Transition probability matrices and correlation ..."
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Cited by 9 (9 self)
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and correlation functions are calculated in order to allow any designer to investigate network performance by means of wellknown analytical techniques.
Research Article Discretetime modeling of Hamiltonian systems
"... Abstract: The problem of discretetime modeling of the lumpedparameter Hamiltonian systems is considered for engineering applications. Hence, a novel gradientbased method is presented, exploiting the discrete gradient concept and the forward Euler discretization under the assumption of the continu ..."
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. The proposed models are convenient for the design of sampleddata controllers. All of the models are considered for several wellknown Hamiltonian systems and the simulation results are demonstrated comparatively. Key words: Hamiltonian systems, discretetime control model, discrete gradient 1.
Solving the DiscreteTime Stochastic Ramsey Model
, 2009
"... This note describes methods for solving deterministic and stochastic versions of the discretetime Ramsey model of economic growth. We derive an iterative procedure for solving the Euler equation and apply it to an example adapted from Pan (2007). The deterministic Ramsey model Consider the followi ..."
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This note describes methods for solving deterministic and stochastic versions of the discretetime Ramsey model of economic growth. We derive an iterative procedure for solving the Euler equation and apply it to an example adapted from Pan (2007). The deterministic Ramsey model Consider
Adaptive interpolation of discretetime signals that can be modeled as autoregressive processes
 IEEE Trans. Acoustics, Speech and Sig. Proc
, 1986
"... AbstractThis paper presents an adaptive algorithm for the restoration of lost sample values in discretetime signals that can locally be described by means of autoregressive processes. The only restrictions are that the positions of the unknown samples should be known and that they should be embedd ..."
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Cited by 22 (2 self)
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AbstractThis paper presents an adaptive algorithm for the restoration of lost sample values in discretetime signals that can locally be described by means of autoregressive processes. The only restrictions are that the positions of the unknown samples should be known and that they should
Discretetime dynamic term structure models with generalized market prices of risk
, 2006
"... This paper develops a rich class of discretetime, nonlinear dynamic term structure models (DTSMs). Under the riskneutral measure, the distribution of the state vector Xt resides within a family of discretetime affine processes that nests the exact discretetime counterparts of the entire class of ..."
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Cited by 17 (0 self)
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This paper develops a rich class of discretetime, nonlinear dynamic term structure models (DTSMs). Under the riskneutral measure, the distribution of the state vector Xt resides within a family of discretetime affine processes that nests the exact discretetime counterparts of the entire class
A Flexible Link Function for DiscreteTime Duration Models
, 2014
"... This paper proposes a discretetime hazard regression approach based on the relation between hazard rate models and excess over threshold models, which are frequently encountered in extreme value modelling. The proposed duration model employs a flexible link function and incorporates the groupeddur ..."
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This paper proposes a discretetime hazard regression approach based on the relation between hazard rate models and excess over threshold models, which are frequently encountered in extreme value modelling. The proposed duration model employs a flexible link function and incorporates the grouped
Troffaes. Dynamic programming for deterministic discretetime systems with uncertain gain
 International Journal of Approximate Reasoning
, 2004
"... We generalise the optimisation technique of dynamic programming for discretetime systems with an uncertain gain function. We assume that uncertainty about the gain function is described by an imprecise probability model, which generalises the wellknown Bayesian, or precise, models. We compare vario ..."
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Cited by 13 (3 self)
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We generalise the optimisation technique of dynamic programming for discretetime systems with an uncertain gain function. We assume that uncertainty about the gain function is described by an imprecise probability model, which generalises the wellknown Bayesian, or precise, models. We compare
Adaptive Switching Gain for a DiscreteTime Sliding Mode Controller
"... Sliding Mode Control is a wellknown technique capable of making the closed loop system robust with respect to certain kinds of parameter variations and unmodeled dynamics. The sliding mode control law consists of the linear control part which is based on the model knowledge and the discontinuous c ..."
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Cited by 1 (1 self)
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uous control part which is based on the model uncertainty. This paper describes two known adaption laws for the switching gain for continuoustime sliding mode controllers. Because these adaption laws have some fundamental problems in discretetime, we introduce a new adaption law specifically designed
Results 1  10
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909