scholarly journals Scalar resonance contributions to the dipion transition rates ofΥ(4S,5S)in the rescattering model

2008 ◽  
Vol 77 (7) ◽  
Author(s):  
Ce Meng ◽  
Kuang-Ta Chao
Keyword(s):  
Transition Rates ◽  
Scalar Resonance

Multi-channel distorted-wave Born approximation for rovibrational transition rates in molecular collisions

2021 ◽  
Vol 155 (3) ◽  
pp. 034105
Author(s):  
Taha Selim ◽  
Arthur Christianen ◽  
Ad van der Avoird ◽  
Gerrit C. Groenenboom
Keyword(s):  
Born Approximation ◽  
Transition Rates ◽  
Distorted Wave ◽  
Distorted Wave Born Approximation ◽  
Molecular Collisions

Transitioning from algae to clay as turbidity agents: Timing, duration, and transition rates for larval sablefish (Anoplopoma fimbria)

Aquaculture ◽  
2021 ◽  
Vol 541 ◽  
pp. 736825
Author(s):  
Jonathan S.F. Lee ◽  
Melissa L. Pierce ◽  
Rachel S. Poretsky ◽  
Matthew A. Cook ◽  
Barry A. Berejikian ◽  
...  
Keyword(s):  
Transition Rates ◽  
Anoplopoma Fimbria

A review of multistate modelling approaches in monitoring disease progression: Bayesian estimation using the Kolmogorov-Chapman forward equations

2021 ◽  
pp. 096228022199750
Author(s):  
Zvifadzo Matsena Zingoni ◽  
Tobias F Chirwa ◽  
Jim Todd ◽  
Eustasius Musenge
Keyword(s):  
Bayesian Estimation ◽  
Transition Probabilities ◽  
Markov Property ◽  
Likelihood Estimation ◽  
Single Individual ◽  
Transition Rates ◽  
Multistate Models ◽  
Time To Event ◽  
Modeling Framework ◽  
Forward Equations

There are numerous fields of science in which multistate models are used, including biomedical research and health economics. In biomedical studies, these stochastic continuous-time models are used to describe the time-to-event life history of an individual through a flexible framework for longitudinal data. The multistate framework can describe more than one possible time-to-event outcome for a single individual. The standard estimation quantities in multistate models are transition probabilities and transition rates which can be mapped through the Kolmogorov-Chapman forward equations from the Bayesian estimation perspective. Most multistate models assume the Markov property and time homogeneity; however, if these assumptions are violated, an extension to non-Markovian and time-varying transition rates is possible. This manuscript extends reviews in various types of multistate models, assumptions, methods of estimation and data features compatible with fitting multistate models. We highlight the contrast between the frequentist (maximum likelihood estimation) and the Bayesian estimation approaches in the multistate modeling framework and point out where the latter is advantageous. A partially observed and aggregated dataset from the Zimbabwe national ART program was used to illustrate the use of Kolmogorov-Chapman forward equations. The transition rates from a three-stage reversible multistate model based on viral load measurements in WinBUGS were reported.

Spectra and E 2 Transition Rates in 116-132 Xe in the IBM

2000 ◽  
Vol 33 (4) ◽  
pp. 593-598 ◽  
Author(s):  
Li ZhongZe ◽  
Liu FengYing ◽  
Ji HuaYing ◽  
Zhang JinFu ◽  
Pak JaeYon
Keyword(s):  
Transition Rates

scholarly journals Denumerable state continuous time Markov decision processes with unbounded cost and transition rates under average criterion

2002 ◽  
Vol 43 (4) ◽  
pp. 541-557 ◽  
Author(s):  
Xianping Guo ◽  
Weiping Zhu
Keyword(s):  
Markov Decision Processes ◽  
Continuous Time ◽  
Decision Processes ◽  
Transition Rates ◽  
Birth And Death Processes ◽  
Optimality Equation ◽  
Average Criterion ◽  
Markov Decision ◽  
Unbounded Cost ◽  
Queue Model

AbstractIn this paper, we consider denumerable state continuous time Markov decision processes with (possibly unbounded) transition and cost rates under average criterion. We present a set of conditions and prove the existence of both average cost optimal stationary policies and a solution of the average optimality equation under the conditions. The results in this paper are applied to an admission control queue model and controlled birth and death processes.

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