Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Nested Sequential Monte Carlo Methods
Authors: Christian Naesseth, Fredrik Lindsten, Thomas Schon
ICML 2015 | Venue PDF | LLM Run Details | Input Tokens: 18,128 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,399 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL Link oping University, Link oping, Sweden Fredrik Lindsten EMAIL The University of Cambridge, Cambridge, United Kingdom Thomas B. Sch on EMAIL 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 signi๏ฌcant computational overhead from running backward sampling) and the bootstrap PF is given N = 10 000. |