Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems
Authors: Lukas Köhs, Bastian Alt, Heinz Koeppl
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our framework under the modeling assumption and compare it against an existing variational inference approach. 4.1. Verification on Ground-Truth Data 1D System We test the method on synthetic data from a one-dimensional two-mode switching dynamical system as specified in Section 2.2. Our Gibbs sampling scheme is able to faithfully recover the latent ground-truth trajectories; both z[0,T ] and y[0,T ] are reproduced with high fidelity, see Fig. 2. 4.2. Inference of Gene-Switching Dynamics We use our framework to infer the switching dynamics of an inducible gene system measured in-house. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Darmstadt, Germany. Correspondence to: Heinz Koeppl <heinz.koeppl@tudarmstadt.de>. |
| Pseudocode | Yes | Algorithm 1 BFFB Gibbs Sampling for Continuous-Time Switching Dynamical Systems |
| Open Source Code | Yes | An implementation of the proposed framework is publicly available. https://git.rwth-aachen.de/bcs/projects/lk/mcmc-ct-sds.git |
| Open Datasets | No | The paper mentions using "synthetic data" and an "inducible gene expression system measured in-house" (Figure 4). While data is used, there is no specific link, DOI, or citation for a publicly available dataset. The synthetic data is generated by the authors, and the gene expression data is in-house. |
| Dataset Splits | No | The paper mentions using "synthetic data" and "in-house" data for verification and inference, respectively, but does not explicitly provide training, validation, or test dataset splits. It evaluates on the full generated/collected data. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, or other libraries/solvers). |
| Experiment Setup | Yes | In all experiments, the hyperparameters are set empirically; for other options, see, e.g. (Casella, 2001). We initialize the Gibbs sampler in the same way to start at reasonable parameter values such as to achieve fast burn-in. All hyperparameters are provided in Appendix D. ... D.1. Hyperparameter Settings We initialize all distribution hyperparameters, cf. Appendix C, empirically. |