Moment-Based Variational Inference for Markov Jump Processes
Authors: Christian Wildner, Heinz Koeppl
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then extend the results to parameter inference and demonstrate the method on several examples. In this section, we apply our method to three examples. We focus on models of the population type for which inference is notoriously difficult due to the unbounded state space. First we consider a linear birth death process. This simple example allows analytic treatment and provides some intuition. Next, we numerically study a stochastic gene expression model of the type that is frequently used in systems biology. As a third example, we consider a stochastic predator prey model to demonstrate our method in combination with moment closure. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering and Information Technology, Technische Universit at Darmstadt, Germany. |
| Pseudocode | Yes | Algorithm 1 Natural Gradient Descent for MB-VI |
| Open Source Code | No | The paper mentions supplementary material for derivations and additional details, but does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | No | The paper states using 'simulated observations' and 'synthetic data' for its examples, implying generated data rather than a pre-existing publicly available dataset. No concrete access information for a dataset is provided. |
| Dataset Splits | No | The paper uses synthetic data for demonstration and does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers. |
| Experiment Setup | No | The paper discusses model-specific parameters for the examples (e.g., c1, c2 for birth-death process) but does not provide concrete experimental setup details for the variational inference algorithm itself, such as specific values for the step size 'h' or other training configurations. |