Implicit Probabilistic Integrators for ODEs
Authors: Onur Teymur, Han Cheng Lie, Tim Sullivan, Ben Calderhead
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We give an illustrative example highlighting the effect of the use of probabilistic integrators including our new method in the setting of parameter inference within an inverse problem. |
| Researcher Affiliation | Academia | Onur Teymur & Ben Calderhead Department of Mathematics Imperial College London; Han Cheng Lie & T.J. Sullivan Institute of Mathematics, Freie Universit at Berlin; & Zuse Institut Berlin |
| Pseudocode | Yes | Pseudo-code for this algorithm is given in the supplementary material. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | We first generate synthetic data Y ; 20 two-dimensional data-points collected at times t Y = 1, 2, . . . , 20 corrupted by centred Gaussian noise with variance σ = (0.01) I2. The paper uses synthetic data and does not provide access information for a publicly available dataset. |
| Dataset Splits | No | The paper describes generating synthetic data and running MCMC, but does not specify explicit training, validation, or test dataset splits or percentages. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions algorithms used (e.g., 'Adaptive Metropolis Hastings', 'pre-conditioned Crank Nicolson algorithm') but does not specify any software libraries or dependencies with version numbers. |
| Experiment Setup | Yes | We first generate synthetic data Y ; 20 two-dimensional data-points collected at times t Y = 1, 2, . . . , 20 corrupted by centred Gaussian noise with variance σ = (0.01) I2. ... Each represents 1000 parameter samples from simulations run with step-sizes h = 0.005, 0.01, 0.02, 0.05. This is made of 11000 total samples, with the first 1000 discarded as burn-in, and the remainder thinned by a factor of 10. ... h = 0.1 and AM0 = 0.2. |