Bias and variance of the Bayesian-mean decoder
Authors: Arthur Prat-Carrabin, Michael Woodford
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 arthur.p@columbia.edu Michael Woodford Department of Economics Columbia University New York, USA mw2230@columbia.edu |
| 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 |