Type-dependent parameter inference

1990 ◽  
Vol 25 (6) ◽  
pp. 127-136 ◽  
Author(s):  
Gordon V. Cormack ◽  
Andrew K. Wright
Keyword(s):  
Parameter Inference ◽  
Dependent Parameter

scholarly journals Neural network aided approximation and parameter inference of non-Markovian models of gene expression

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qingchao Jiang ◽  
Xiaoming Fu ◽  
Shifu Yan ◽  
Runlai Li ◽  
Wenli Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Stochastic Dynamics ◽  
Stochastic Simulations ◽  
Parameter Inference ◽  
Biochemical Processes ◽  
Markovian Models ◽  
The Neural Network ◽  
Large Numbers ◽  
Small Set ◽  
Noisy Measurements

AbstractNon-Markovian models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models, as well as the inference of their parameters from data, are fraught with difficulties because the dynamics depends on the system’s history. Here we use an artificial neural network to approximate the time-dependent distributions of non-Markovian models by the solutions of much simpler time-inhomogeneous Markovian models; the approximation does not increase the dimensionality of the model and simultaneously leads to inference of the kinetic parameters. The training of the neural network uses a relatively small set of noisy measurements generated by experimental data or stochastic simulations of the non-Markovian model. We show using a variety of models, where the delays stem from transcriptional processes and feedback control, that the Markovian models learnt by the neural network accurately reflect the stochastic dynamics across parameter space.

scholarly journals A Stable Isotope Approach for Estimating the Contribution of Recycled Moisture to Precipitation in Lanzhou City, China

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1783
Author(s):  
Fenli Chen ◽  
Mingjun Zhang ◽  
Xixi Wu ◽  
Shengjie Wang ◽  
Athanassios A. Argiriou ◽  
...  
Keyword(s):  
Environmental Changes ◽  
Water Cycle ◽  
Summer Precipitation ◽  
Northwest China ◽  
Plant Transpiration ◽  
Surface Evaporation ◽  
Lanzhou City ◽  
Water Vapors ◽  
Spatial Differences ◽  
Dependent Parameter

The proportional contribution of recycled moisture to local precipitation is a geographically dependent parameter that cannot be ignored in water budgets. Stable hydrogen and oxygen isotopes are sensitive to environmental changes and can be applied to investigate the modern water cycle. In this study, a three-component mixing model is used to calculate the contribution of different water vapors (advection, evaporation and transpiration) to summer precipitation in Lanzhou city, Northwest China. The results show that for all sampling sites in Lanzhou, the contribution of advection vapor to precipitation is the largest, followed by the plant transpiration vapor, and the contribution of surface evaporation water vapor is usually the least, with the average values of 87.96%, 9.1% and 2.9%, respectively. The spatial differences of plant transpiration vapor are generally larger than those of advection vapor and surface evaporation vapor, and the high values appear in Yongdeng, Daheng and Gaolan.

scholarly journals Modified Filtered-X Hierarchical LMS Algorithm with Sequential Partial Updates for Active Noise Control

2020 ◽  
Vol 11 (1) ◽  
pp. 344
Author(s):  
Pedro Ramos Lorente ◽  
Raúl Martín Ferrer ◽  
Fernando Arranz Martínez ◽  
Guillermo Palacios-Navarro
Keyword(s):  
Computational Complexity ◽  
Convergence Rate ◽  
Noise Control ◽  
Active Noise Control ◽  
Adaptive Strategy ◽  
Lms Algorithm ◽  
Step Size ◽  
Periodic Disturbances ◽  
Active Noise ◽  
Dependent Parameter

In the field of active noise control (ANC), a popular method is the modified filtered-x LMS algorithm. However, it has two drawbacks: its computational complexity higher than that of the conventional FxLMS, and its convergence rate that could still be improved. Therefore, we propose an adaptive strategy which aims at speeding up the convergence rate of an ANC system dealing with periodic disturbances. This algorithm consists in combining the organization of the filter weights in a hierarchy of subfilters of shorter length and their sequential partial updates (PU). Our contribution is threefold: (1) we provide the theoretical basis of the existence of a frequency-dependent parameter, called gain in step-size. (2) The theoretical upper bound of the step-size is compared with the limit obtained from simulations. (3) Additional experiments show that this strategy results in a fast algorithm with a computational complexity close to that of the conventional FxLMS.

scholarly journals Parameter inference for a stochastic kinetic model of expanded polyglutamine proteins

Biometrics ◽  
2021 ◽  
Author(s):  
H. F. Fisher ◽  
R. J. Boys ◽  
C. S. Gillespie ◽  
C. J. Proctor ◽  
A. Golightly
Keyword(s):  
Kinetic Model ◽  
Parameter Inference

scholarly journals Identification and control of electro-mechanical systems using state-dependent parameter estimation

2016 ◽  
Vol 90 (4) ◽  
pp. 643-660 ◽  
Author(s):  
Alexandre Janot ◽  
Peter C. Young ◽  
Maxime Gautier
Keyword(s):  
Parameter Estimation ◽  
Mechanical Systems ◽  
State Dependent ◽  
Dependent Parameter ◽  
And Control

The solution of an infinite set of differential-difference equations occurring in polymerization and queueing problems

1963 ◽  
Vol 59 (1) ◽  
pp. 117-124 ◽  
Author(s):  
A. Wragg
Keyword(s):  
Difference Equations ◽  
Queueing Theory ◽  
Bessel Functions ◽  
Formal Solution ◽  
Monomer Concentration ◽  
Arrival Rate ◽  
Time Dependent ◽  
Type Integral ◽  
Dependent Parameter ◽  
Infinite Set

AbstractThe time-dependent solutions of an infinite set of differential-difference equations arising from queueing theory and models of ‘living’ polymer are expressed in terms of modified Bessel functions. Explicit solutions are available for constant values of a parameter describing the arrival rate or monomer concentration; for time-dependent parameter a formal solution is obtained in terms of a function which satisfies a Volterra type integral equation of the second kind. These results are used as the basis of a numerical method of solving the infinite set of differential equations when the time-dependent parameter itself satisfies a differential equation.

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