Nested Sequential Monte Carlo Methods
Authors: Christian Naesseth, Fredrik Lindsten, Thomas Schon
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate NSMC on three high-dimensional examples, both with real and synthetic data. We compare NSMC with standard (bootstrap) PF and the ST-PF of Beskos et al. (2014a) with equal computational budgets on a single machine (i.e., neglecting the fact that NSMC is more easily distributed). |
| Researcher Affiliation | Academia | Christian A. Naesseth CHRISTIAN.A.NAESSETH@LIU.SE Link oping University, Link oping, Sweden Fredrik Lindsten FREDRIK.LINDSTEN@ENG.CAM.AC.UK The University of Cambridge, Cambridge, United Kingdom Thomas B. Sch on THOMAS.SCHON@IT.UU.SE Uppsala University, Uppsala, Sweden |
| Pseudocode | Yes | Algorithm 1 Nested IS, Algorithm 2 SMC (fully adapted), Algorithm 3 Nested SMC (fully adapted), Algorithm 4 Backward simulator (fully adapted) |
| Open Source Code | Yes | Code available at https://github.com/can-cs/nestedsmc |
| Open Datasets | Yes | Jones, P.D. and Harris, I. CRU TS3.21: Climatic research unit (CRU) time-series (ts) version 3.21 of high resolution gridded data of month-bymonth variation in climate (jan. 1901dec. 2012). NCAS British Atmospheric Data Centre, sep 2013. URL http://dx.doi.org/10.5285/ D0E1585D-3417-485F-87AE-4FCECF10A992. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits. It describes the time range of the data used for different models, but not how it was partitioned for training or validation. |
| Hardware Specification | No | The paper states that experiments were run "on a single machine" but does not provide specific hardware details such as CPU/GPU models, memory, or other computational resources used. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers (e.g., Python, PyTorch, TensorFlow versions, or solver versions) used for its implementation or experiments. |
| Experiment Setup | Yes | We use N = 500 and M = 2 d for NSMC and match the computational time for ST-PF and bootstrap PF. We use N = M = 100 for both ST-PF and NSMC (the special structure of this model implies that there is no significant computational overhead from running backward sampling) and the bootstrap PF is given N = 10 000. |