Asynchronous Anytime Sequential Monte Carlo
Authors: Brooks Paige, Frank Wood, Arnaud Doucet, Yee Whye Teh
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | 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 {brooks,fwood}@robots.ox.ac.uk Arnaud Doucet Yee Whye Teh Department of Statistics University of Oxford Oxford, UK {doucet,y.w.teh}@stats.ox.ac.uk |
| 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. |