Bayesian Parameter Estimation for Nonlinear Dynamics Using Sensitivity Analysis
Authors: Yi Chou, Sriram Sankaranarayanan
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on a variety of nonlinear benchmarks and compare our results with Markov Chain Monte Carlo and Sequential Monte Carlo approaches. |
| Researcher Affiliation | Academia | Yi Chou and Sriram Sankaranarayanan University of Colorado, Boulder, USA {Yi.Chou, Sriram.Sankaranarayanan}@colorado.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions that the ideas were implemented in C++ but does not provide any concrete access to the source code, nor does it state that the code is open-source or publicly available. |
| Open Datasets | No | For each benchmark a ground truth parameter value, given in Table 1, was fixed to generate simulations from a fixed initial condition. One of the state variables is taken to be the output and thus the data is generated. |
| Dataset Splits | No | The paper describes internal partitioning and refinement of the parameter space for its Bayesian inference approach, but does not specify training, validation, or test dataset splits in the conventional sense for reproducibility of data partitioning. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments. |
| Software Dependencies | No | The paper states that the implementation uses 'C++ programming language' and the 'ODEINT ODE solver', but it does not specify version numbers for either, nor does it list other software dependencies with versions. |
| Experiment Setup | Yes | The paper provides details such as the sampling distribution (zero mean Gaussian with adjustable variance), specific prior distributions (e.g., uniform over [7,12] for parameter g), initial parameter values for simulations (e.g., θ1 = 0.3 and θ2 = 0.15), and specifics of the adaptive refinement strategy including criteria like 'logumax loguj W' and a relative difference threshold of '0.1'. |