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GENERATING MARKOV EVOLUTIONARY MATRICES FOR A GIVEN BRANCH LENGTH

by Marta Casanellas, Anna Kedzierska
"... 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 continuous-time models, it is easy to simulate the process for any give ..."
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given branch length. For discrete-time models, it is not so trivial. In this paper we solve this problem for the most well-known discrete-time 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 Discrete-Time

by Systems With Uncertain, G. De Cooman, M. C. M. Troffaes
"... 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 well-known 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 well-known Bayesian, or precise, models. We compare

A Discrete-Time Model for Multimedia Correlated Sources

by A. La Corte, A. Lombardo, G. Schembra , 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 ..."
Abstract - Cited by 9 (9 self) - Add to MetaCart
and correlation functions are calculated in order to allow any designer to investigate network performance by means of well-known analytical techniques.

Research Article Discrete-time modeling of Hamiltonian systems

by Turk J Elec Eng, Comp Sci, Salman Kurtulan
"... Abstract: The problem of discrete-time modeling of the lumped-parameter Hamiltonian systems is considered for engineering applications. Hence, a novel gradient-based 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 sampled-data controllers. All of the models are considered for several well-known Hamiltonian systems and the simulation results are demonstrated comparatively. Key words: Hamiltonian systems, discrete-time control model, discrete gradient 1.

Solving the Discrete-Time Stochastic Ramsey Model

by Chris Elbers, Chris Elbers , 2009
"... This note describes methods for solving deterministic and stochas-tic versions of the discrete-time 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 stochas-tic versions of the discrete-time 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 discrete-time signals that can be modeled as autoregressive processes

by J. E. M. Janssen, Raymond N. J. Veldhuis, Lodewijk, B. Vries - IEEE Trans. Acoustics, Speech and Sig. Proc , 1986
"... Abstract-This paper presents an adaptive algorithm for the restoration of lost sample values in discrete-time 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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Abstract-This paper presents an adaptive algorithm for the restoration of lost sample values in discrete-time 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

Discrete-time dynamic term structure models with generalized market prices of risk

by Qiang Dai, Anh Le, Kenneth J. Singleton , 2006
"... This paper develops a rich class of discrete-time, nonlinear dynamic term structure models (DTSMs). Under the risk-neutral measure, the distribution of the state vector Xt resides within a family of discrete-time affine processes that nests the exact discrete-time counterparts of the entire class of ..."
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This paper develops a rich class of discrete-time, nonlinear dynamic term structure models (DTSMs). Under the risk-neutral measure, the distribution of the state vector Xt resides within a family of discrete-time affine processes that nests the exact discrete-time counterparts of the entire class

A Flexible Link Function for Discrete-Time Duration Models

by Wolfgang Hess, Gerhard Tutz, Jan Gertheiss, Wolfgang Hess, Gerhard Tutz , 2014
"... This paper proposes a discrete-time 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-dur ..."
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This paper proposes a discrete-time 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 discrete-time systems with uncertain gain

by Gert De Cooman, Matthias C. M. Troffaes - 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 well-known Bayesian, or precise, models. We compare vario ..."
Abstract - Cited by 13 (3 self) - Add to MetaCart
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 well-known Bayesian, or precise, models. We compare

Adaptive Switching Gain for a Discrete-Time Sliding Mode Controller

by G. Monsees, J. M. A. Scherpen
"... Sliding Mode Control is a well-known 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 discontin-uous c ..."
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-uous control part which is based on the model uncertainty. This paper describes two known adaption laws for the switch-ing gain for continuous-time sliding mode controllers. Be-cause these adaption laws have some fundamental problems in discrete-time, we introduce a new adaption law specifically designed
Results 1 - 10 of 909