Researchers explore AI's role in scientific modeling, from historical breakthroughs to modern Bayesian frameworks like ModelSMC.

Anthropic published a guest research essay on November 6, 2026, in which AI for Science grantees Jakob Macke, Daniel Gedon ...

Bayesian inference offers a principled mechanism for updating beliefs by combining prior distributions with observed data via Bayes’s theorem. Central to this approach is the specification of a ...

We will take a deep dive into reliable AI-related papers published on arXiv around the last day (around October 21, 2026), ...

Inverse problems arise when one seeks to recover unknown parameters or functions from indirect, noisy observations via a forward model. The Bayesian framework casts this recovery as the updating of a ...

Researchers have developed an importance-sampling-based black box variational inference algorithm that reuses earlier ...

Are Induction and Deduction Not Enough?Hybrid AI (Neuro-Symbolic AI), which combines the flexibility of neural networks with the rigor of symbolic logic, is attracting attention.The idea is to combine ...