Efficient and Flexible Inference for Stochastic Systems
Authors: Stefan Bauer, Nico S. Gorbach, Djordje Miladinovic, Joachim M. Buhmann
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare our approach on two established benchmark models for stochastic systems especially used for weather forecasts. The runtime for state estimation using the approach of Vrettas et al. [2011] and our method is indicated in table 1. We use our method to infer the states and drift parameters for the Lorenz attractor where the dimension y is unobserved. The estimated state trajectories are shown in figure 4. |
| Researcher Affiliation | Academia | Stefan Bauer Department of Computer Science ETH Zurich bauers@inf.ethz.chNico S. Gorbach Department of Computer Science ETH Zurich ngorbach@inf.ethz.chÐor de Miladinovi c Department of Computer Science ETH Zurich djordjem@inf.ethz.chJoachim M. Buhmann Department of Computer Science ETH Zurich jbuhmann@inf.ethz.ch |
| Pseudocode | Yes | Algorithm 1 Ensemble based parameter estimation for SDEs |
| Open Source Code | No | No explicit statement or link providing concrete access to the source code for the methodology described in this paper was found. |
| Open Datasets | No | No concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset was provided. The paper refers to benchmark models (Lorenz96, Lorenz attractor) which are simulated, not datasets with specified public access. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning was provided. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments were provided. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) were provided. |
| Experiment Setup | No | No specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) were found in the main text. |