Differentiable Likelihoods for Fast Inversion of ’Likelihood-Free’ Dynamical Systems
Authors: Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that these methods outperform standard likelihood-free approaches on three benchmark-systems. |
| Researcher Affiliation | Collaboration | 1University of T ubingen, T ubingen, Germany 2Max Planck Institute for Intelligent Systems, T ubingen, Germany 3Bosch Center for Artificial Intelligence, Renningen, Germany. |
| Pseudocode | Yes | Algorithm 1 Gradient-based sampling/optimization |
| Open Source Code | No | The paper does not provide an explicit statement or link to the open-source code for the described methodology. |
| Open Datasets | No | All datasets are, as in eq. (3), generated by adding Gaussian noise to the solution xθ for some true parameter θ . ... To generate data by eq. (3), we added Gaussian noise with variance σ2 = 0.01 to the corresponding solution at time points [0.5, 1, 1.5, 2, 2.5, 3., 3.5, 4., 4.5]. |
| Dataset Splits | No | The paper does not explicitly describe training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | The optimizers and samplers were initialized at θ0 = [0.8, 0.2, 0.05, 1.1], and the forward solutions for all likelihood evaluations were computed with step size h = 0.05. In order to turn this θ0 into a useful initialization for the Markov chains, we accepted the first 45 states generated by PHMC and PLMC... For all optimizers, we picked the best the step size and, for all samplers, the best proposal width within the interval [10 16, 100] which is wide enough to contain all plausible values. |