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- W2594735566 abstract "This note is concerned with accurate and computationally efficient approximations of moments of Gaussian random variables passed through sigmoid or softmax mappings. These approximations are semi-analytical (i.e. they involve the numerical adjustment of parametric forms) and highly accurate (they yield 5% error at most). We also highlight a few niche applications of these approximations, which arise in the context of, e.g., drift-diffusion models of decision making or non-parametric data clustering approaches. We provide these as examples of efficient alternatives to more tedious derivations that would be needed if one was to approach the underlying mathematical issues in a more formal way. We hope that this technical note will be helpful to modellers facing similar mathematical issues, although maybe stemming from different academic prospects." @default.
- W2594735566 created "2017-03-16" @default.
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- W2594735566 date "2017-03-01" @default.
- W2594735566 modified "2023-09-27" @default.
- W2594735566 title "Semi-analytical approximations to statistical moments of sigmoid and softmax mappings of normal variables" @default.
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