A Bayesian perspective on orientation estimation in cryo-EM is presented, with the minimum mean-square error estimator outperforming standard cross-correlation-based approaches, particularly under challenging low signal-to-noise conditions. We demonstrate that improved orientation estimation has a decisive impact on 3D reconstruction quality and structural heterogeneity recovery.

Tübingen, Germany (July 14-16, 2025)

Transmitting Science ONLINE course Introduction to Bayesian Inference in Practice, by Daniele Silvestro and Tobias Andermann.

Many questions in experimental mathematics are fundamentally inductive in nature. Here we demonstrate how Bayesian inference—the logic of partial beliefs—can be used to quantify the evidence that finite data provide in favor of a general law. As a concrete example we focus on the general law which posits that certain fundamental constants (i.e., the irrational numbers \pi, e, \sqrt{2}, and ln 2) are normal; specifically, we consider the more restricted hypothesis that each digit in the constant’s decimal expansion occurs equally often. Our analysis indicates that for each of the four constants, the evidence in favor of the general law is overwhelming. We argue that the Bayesian paradigm is particularly apt for applications in experimental mathematics, a field in which the plausibility of a general law is in need of constant revision in light of data sets whose size is increasing continually and indefinitely.

Transmitting Science ONLINE course Introduction to Bayesian Inference in Practice, by Daniele Silvestro and Tobias Andermann.

Humans and chimpanzees share the potential to rationally revise their beliefs

Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...