Timezone: »
We consider the problem of performing Bayesian inference in probabilistic models where observations are accompanied by uncertainty, referred to as "uncertain evidence.'' We explore how to interpret uncertain evidence, and by extension the importance of proper interpretation as it pertains to inference about latent variables. We consider a recently-proposed method "distributional evidence'' as well as revisit two older methods: Jeffrey's rule and virtual evidence. We devise guidelines on how to account for uncertain evidence and we provide new insights, particularly regarding consistency. To showcase the impact of different interpretations of the same uncertain evidence, we carry out experiments in which one interpretation is defined as "correct.'' We then compare inference results from each different interpretation illustrating the importance of careful consideration of uncertain evidence.
Author Information
Andreas Munk (University of British Columbia)
Alexander Mead
Frank Wood (UBC + inverted.ai)
Related Events (a corresponding poster, oral, or spotlight)
-
2023 Poster: Uncertain Evidence in Probabilistic Models and Stochastic Simulators »
Tue. Jul 25th 09:00 -- 11:30 PM Room Exhibit Hall 1 #322
More from the Same Authors
-
2023 : Visual Chain-of-Thought Diffusion Models »
William Harvey · Frank Wood -
2023 : Scaling Graphically Structured Diffusion Models »
Christian Weilbach · William Harvey · Hamed Shirzad · Frank Wood -
2023 Poster: Graphically Structured Diffusion Models »
Christian Weilbach · William Harvey · Frank Wood -
2023 Oral: Graphically Structured Diffusion Models »
Christian Weilbach · William Harvey · Frank Wood -
2021 Poster: Robust Asymmetric Learning in POMDPs »
Andrew Warrington · Jonathan Lavington · Adam Scibior · Mark Schmidt · Frank Wood -
2021 Oral: Robust Asymmetric Learning in POMDPs »
Andrew Warrington · Jonathan Lavington · Adam Scibior · Mark Schmidt · Frank Wood -
2020 Poster: All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference »
Rob Brekelmans · Vaden Masrani · Frank Wood · Greg Ver Steeg · Aram Galstyan -
2019 Poster: Amortized Monte Carlo Integration »
Adam Golinski · Frank Wood · Tom Rainforth -
2019 Oral: Amortized Monte Carlo Integration »
Adam Golinski · Frank Wood · Tom Rainforth -
2018 Poster: Deep Variational Reinforcement Learning for POMDPs »
Maximilian Igl · Luisa Zintgraf · Tuan Anh Le · Frank Wood · Shimon Whiteson -
2018 Oral: Deep Variational Reinforcement Learning for POMDPs »
Maximilian Igl · Luisa Zintgraf · Tuan Anh Le · Frank Wood · Shimon Whiteson