Guaranteed bounds on posterior distributions of discrete probabilistic programs with loops
We study the problem of bounding the posterior distribution of discrete probabilistic programs with unbounded support, loops, and conditioning. Loops pose the main difficulty in this setting: even if exact Bayesian inference is possible, the state of the art requires user-provided loop invariant tem...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Association for Computing Machinery
2024
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