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Activity Number: 245 - Bayesian Models for Clustering and Latent Allocation
Type: Contributed
Date/Time: Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #323562
Title: Forecasting with perturbed data
Author(s): Tahir Ekin* and William Nick Caballero and Roi Naveiro and David Rios Insua
Companies: Texas State University and The United States Air Force Academy and ICMAT-CSIC and ICMAT-CSIC
Keywords: adversarial forecasting; adversarial risk analysis; hidden Markov models; Bayesian decision theory

This manuscript focuses on the impact of adversarial perturbations on forecasts where an attacker manipulates a batch of data before it is observed by the defender. The proposed Bayesian decision models are based on adversarial risk analysis allowing incomplete information. We demonstrate the proposed framework using hidden Markov Models, and discuss potential applications.

Authors who are presenting talks have a * after their name.

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