Research map / Probabilistic and scientific modeling / Variational and generative sampling
Approximate Probabilistic Inference via Word-Level Counting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use word-level universal hashing and SMT reasoning instead of bit-blasting with CDM | Approximate model counting on word-level benchmarks · 1160877, 1160530, 1159005, 1160300, 1159391, 1159520 | runtime (s): 2.57 vs 44.01; 2.01 vs 43.28; 28.88 vs 105.6; 44.02 vs 71.16; 57.03 vs 91.62; 114.53 vs 155.09 | Helped |
| Use word-level universal hashing and SMT reasoning instead of bit-blasting with CDM | Approximate model counting on word-level benchmarks · 1159708, 1159472, 1159115, 1159431, 1160191 | completion under timeout | Helped |
| Use word-level universal hashing and SMT reasoning instead of bit-blasting with CDM | Approximate model counting on word-level benchmarks · squaring27 | runtime (s): -- vs 2998.97 | Hurt |
| Use word-level universal hashing and SMT reasoning instead of bit-blasting with CDM | Approximate model counting on word-level benchmarks · squaring51 | runtime (s): 3285.52 vs 607.22 | Hurt |
| Use word-level universal hashing and SMT reasoning instead of bit-blasting with CDM | Approximate model counting on word-level benchmarks · Benchmarks with heterogeneous widths, extracts, or multiplication | runtime | Mixed |
| Use SMTApproxMC to estimate counts | SMTApproxMC · Selected benchmarks compared with sharpSATTool exact counts | count accuracy: observed geometric tolerance($\varepsilon_{obs}$) = $0.04$ | Helped |
| Use word-level hash functions rather than Boolean XOR hashes | Approximate counting over fixed-width words · Word-level model-counting problems | runtime | Mixed |
Closest papers by meaning
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- Engineering an Exact Pseudo-Boolean Model Counter (AAAI 2024 · similarity 0.87)
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- On the Hardness of Approximating Distributions with Tractable Probabilistic Models (NeurIPS 2025 · similarity 0.85)
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