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