Work

Robust latent data representations

Year: 2024

Type: article

Abstract: Economic agents often infer latent structures—such as preference types— from data, without exogenously specified priors. We model such agents as empirical Bayesians. They estimate both the prior over types and the meanings of types via maximum likelihood. We show this estimation is equivalent to decomposing the sample into subsamples, each best explained by a single available latent type, with the decomposition minimizing the average misfit. The equivalence yields structural properties: optimal latent representations are robust (type definitions locally invariant to data changes) and simple (type count bounded). We extend these properties to agents who face frictions in evaluating likelihoods.

Source: Zurich Open Repository and Archive (University of Zurich)

DOI: 10.5167/uzh-283351

Authors: Samuelson, Larry, Steiner, Jakub

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7126074549

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