Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Asynchronous Anytime Sequential Monte Carlo
Authors: Brooks Paige, Frank Wood, Arnaud Doucet, Yee Whye Teh
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report experiments on performing inference in two simple state space models, each with N = 50 observations, in order to demonstrate the overall validity and utility of the particle cascade algorithm. |
| Researcher Affiliation | Academia | Brooks Paige Frank Wood Department of Engineering Science University of Oxford Oxford, UK EMAIL Arnaud Doucet Yee Whye Teh Department of Statistics University of Oxford Oxford, UK EMAIL |
| Pseudocode | No | The paper describes the algorithm using text and equations but does not include structured pseudocode or an algorithm block. |
| 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 | The paper mentions using a hidden Markov model and a linear Gaussian model but does not provide specific access information (link, DOI, repository, or formal citation with authors/year) for publicly available datasets used in the experiments. |
| Dataset Splits | No | The paper does not specify exact dataset split percentages, sample counts, or describe a cross-validation setup for reproducibility. |
| Hardware Specification | Yes | These particular experiments were all run on Amazon EC2, in an 8-core environment with Intel Xeon E5-2680 v2 processors. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We report experiments on performing inference in two simple state space models, each with N = 50 observations... In all benchmarks, we propose from the prior distribution, with q(xn| ) f(xn|x0:n 1); the SMC and i CSMC benchmarks use a multinomial resampling scheme. |