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..
Bias and variance of the Bayesian-mean decoder
Authors: Arthur Prat-Carrabin, Michael Woodford
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To assess the quality of the approximations presented above, we run simulations of an encodingdecoding process, and compare the approximated and true values of the bias and variance of the Bayesian-mean decoder, with different efficient encodings, and under different amounts of imprecision in the encoding. |
| Researcher Affiliation | Academia | Arthur Prat-Carrabin Department of Economics Columbia University New York, USA EMAIL Michael Woodford Department of Economics Columbia University New York, USA EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | We study a case in which a normally-distributed stimulus is encoded through a normally-distributed representation. Specifically, the prior is a Gaussian distribution with mean m and standard deviation σ, i.e., x N(m, σ2). |
| Dataset Splits | No | The paper describes running simulations of a generative process and comparing analytical approximations to true values, but does not mention specific training, validation, or test dataset splits. |
| Hardware Specification | No | The paper mentions running simulations but does not provide any specific details about the hardware used, such as GPU/CPU models or other system specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers used for the simulations, only general statements about numerical computation. |
| Experiment Setup | Yes | We study a case in which a normally-distributed stimulus is encoded through a normally-distributed representation. Specifically, the prior is a Gaussian distribution with mean m and standard deviation σ, i.e., x N(m, σ2). ... We run simulations in which the encoding noise, ν, spans a range of values: ν = 0.005, 0.01, 0.02, 0.05, and 0.1 |