Development of the MCR Method for Estimation of Parameters in Continuous Time Markov Chain Models

Joyner, Michelle Lynn; Robacker, Thomas C

doi:10.12732/ijpam.v112i2.15

Cited by 2 publications

(1 citation statement)

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“…al. [21] further tested this methodology and determined that if the population size is 'sufficiently large' (the concept of 'sufficiently large' is model specific), this parameter estimation method produces good estimates. Using the deterministic approximation, Banks and Joyner explained in [11] how one could extend this concept to model comparison for nested stochastic models.…”

Section: Approximation Of Stochastic Models By Deterministic Systemsmentioning

confidence: 99%

Adaption of Akaike information criterion under least squares frameworks for comparison of stochastic models

Banks

Joyner

2019

Quart. Appl. Math.

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In this paper, we examine the feasibility of extending the Akaike Information Criterion (AIC) for deterministic systems as a potential model selection criteria for stochastic models. We discuss the implementation method for three different classes of stochastic models: continuous time Markov chains (CTMC), stochastic differential equations (SDE), and random differential equations (RDE). The effectiveness and limitations of implementing the AIC for comparison of stochastic models is demonstrated using simulated data from the three types of models and then applied to experimental longitudinal growth data for algae.

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Section: Approximation Of Stochastic Models By Deterministic Systemsmentioning

confidence: 99%

Adaption of Akaike information criterion under least squares frameworks for comparison of stochastic models

Banks

Joyner

2019

Quart. Appl. Math.

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Parameter optimisation using Bayesian inference for spallation models

Hirtz,

David,

Cugnon

et al. 2024

Eur. Phys. J. A

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The accuracy and precision of high-energy spallation models are key issues for the design and development of new applications and experiments. We present a method to estimate model parameters and associated uncertainties by leveraging the Bayesian version of the Generalised Least Squares method, which enables us to incorporate prior knowledge on the parameter values. This approach is designed to adjust parameters based on experimental data, accounting for experimental uncertainty information, and providing uncertainties for all adjusted parameters. This approach is designed in order both to improve the accuracy of models through the modification of free parameters of these models, which results in a better reproduction of experimental data, and to estimate the uncertainties of these parameters and, by extension, their impacts on the model output. We aim at demonstrating the Generalised Least Square method can be applied in the case of Monte Carlo models. We present a proof-of-concept for Monte Carlo models in the specific case of nuclear physics with the model combination INCL/ABLA. We discuss the challenges in the application of this method to high-energy spallation models, notably the large runtime and the stochasticity of the models. Our results indicate this framework can also be applied to analogous situations where parameters of a computationally expensive Monte Carlo code should be inferred/improved.

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Development of the MCR Method for Estimation of Parameters in Continuous Time Markov Chain Models

Cited by 2 publications

References 20 publications

Adaption of Akaike information criterion under least squares frameworks for comparison of stochastic models

Adaption of Akaike information criterion under least squares frameworks for comparison of stochastic models

Parameter optimisation using Bayesian inference for spallation models

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