There might be an even cheaper way to write down your specific case, because it seems like you actually have multiple VARs that share A but not B? I don’t fully understand but if you have a more concrete example there might be even more speed to wring out of the system. The way I wrote it here is that everything relates to everything. If in essence \bar{A} is also block diagonal, then some of the terms in R^T R also simplify, but a lot of this pytensor should automatically handle for you. Still, it’s nice to know.
| # | Наименование новости | Тональность | Информативность | Дата публикации |
|---|---|---|---|---|
| 1 | Did state-space models change the way they handle time-varying matrices? | 0 | 21.76 | 30-09-2026 |
| 2 | Contributions to State-Space Models & Project Ideas for PyMC-Extras | 0 | 14.13 | 14-09-2026 |
| 3 | 🚀 Release pymc-extras v0.15.0 | 0 | 19.63 | 11-09-2026 |
| 4 | New contributor looking for guidance: from issue fixes to sustained PyMC contributions | 0 | 10.39 | 12-09-2026 |
| 5 | Proposal / feedback: topology-aware posterior predictive and simulator summaries | 0 | 11.43 | 04-10-2026 |
| 6 | Setting and justifying priors for a discrete "what went wrong" model when I have no labeled data | 0 | 11.32 | 03-09-2026 |
| 7 | Bug in nutpie+numba: memory needed for LogNormal compilation scales superlinearly | 0 | 10 | 17-08-2026 |
| 8 | 🚀 Release pymc-extras v0.15.1 | 0 | 19.63 | 16-09-2026 |
| 9 | Indexing for vector variable | 0 | 10.89 | 16-09-2026 |
| 10 | Sampling PyMC models in JupyterLite with a WebAssembly backend for PyTensor | 0 | 6.38 | 28-09-2026 |