Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Bayesian Learning of Sum-Product Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Bayesian SPN structure learning instead of LearnSPN | Bayesian SPN · discrete benchmarks | test log-likelihood: -40.50 -> -39.77 (Audio) | Helped |
| Bayesian SPN instead of ID-SPN | Bayesian SPN · discrete benchmarks | test log-likelihood: -39.79 -> -39.77 (Audio); -26.98 -> -34.10 (Accidents) | Mixed |
| use infinite mixture of Bayesian SPNs instead of finite Bayesian SPN | Bayesian SPN · discrete benchmarks | test log-likelihood: -51.66 -> -50.94 (EachMovie); -52.42 -> -52.86 (Jester) | Mixed |
| Bayesian SPN instead of mixed SPN (MSPN) on heterogeneous data | Bayesian SPN · heterogeneous datasets | test log-likelihood: -39.20 -> -0.47 (Autism); -0.13 -> -8.62 (Wine) | Mixed |
| Bayesian SPN instead of ABDA on heterogeneous data | Bayesian SPN · heterogeneous datasets | test log-likelihood: -5.91 -> -4.62 (Adult); -17.48 -> -21.21 (Diabetes) | Mixed |
| Bayesian SPN with 50% missing dimensions versus LearnSPN/ID-SPN with deletion or kNN imputation | Bayesian SPN · EachMovie, WebKB, BBC with missing values | test log-likelihood | Helped |
Closest papers by meaning
- Collapsed Variational Inference for Sum-Product Networks (ICML 2016 · similarity 0.91)
- Linear Time Computation of Moments in Sum-Product Networks (NeurIPS 2017 · similarity 0.90)
- A Unified Approach for Learning the Parameters of Sum-Product Networks (NeurIPS 2016 · similarity 0.90)
- Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs (ICLR 2024 · similarity 0.86)
- Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks (ICLR 2024 · similarity 0.86)
- Inexact Column Generation for Bayesian Network Structure Learning via Difference-of-Submodular Optimization (NeurIPS 2025 · similarity 0.85)
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