Hi! Following this thread with interest — I’m also new here, coming from PyTorch-Ignite where I’ve got a merged PR. Given what you mentioned about needing to actually use the tools first: any suggestions for a good beginner-friendly Bayesian modeling exercise, or should I look at doc/issue contributions first while I build that familiarity?
| # | Наименование новости | Тональность | Информативность | Дата публикации |
|---|---|---|---|---|
| 1 | Introduction & GSoC 2027 Interest — PR #8434 (CAR distribution batch support) | 0 | 10.65 | 16-09-2026 |
| 2 | Contributions to State-Space Models & Project Ideas for PyMC-Extras | 0 | 14.13 | 14-09-2026 |
| 3 | Proposal / feedback: topology-aware posterior predictive and simulator summaries | 0 | 11.43 | 04-10-2026 |
| 4 | 🚀 Release pymc-extras v0.15.1 | 0 | 19.63 | 16-09-2026 |
| 5 | 🚀 Release v6.3.2 | 0 | 18.52 | 08-09-2026 |
| 6 | 🚀 Release pymc-extras v0.15.0 | 0 | 19.63 | 11-09-2026 |
| 7 | Setting and justifying priors for a discrete "what went wrong" model when I have no labeled data | 0 | 11.32 | 03-09-2026 |
| 8 | Sampling PyMC models in JupyterLite with a WebAssembly backend for PyTensor | 0 | 6.38 | 28-09-2026 |
| 9 | Save and Load a BART model | 0 | 12.34 | 28-09-2026 |
| 10 | What's the best way to fit a statespace model to multiple time series | 0 | 4.76 | 11-09-2026 |