Statistics articles from across Nature Portfolio

Multi-model ensembles of infectious disease forecasts tend to perform better than individual forecasts, but assigning appropriate weights to component models in the ensemble is challenging. Here, the authors present epiFFORMA, a method for automatic component weighting that does not require historical data.

The second law of thermodynamics says that entropy may only ever increase during the conversion of one physical state into another. Finding an analogous quantity to characterize the conversion of entangled quantum states has been a rollercoaster ride.

Decision-making inherently involves cause–effect relationships that introduce causal challenges. We argue that reliable algorithms for decision-making need to build upon causal reasoning. Addressing these causal challenges requires explicit assumptions about the underlying causal structure to ensure identifiability and estimatability, which means that the computational methods must successfully align with decision-making objectives in real-world tasks.

We propose a diversity-aware population modeling framework using Bayesian multilevel regression and post-stratification to quantify sociodemographic disparities in cognitive development. Our approach improved subgroup estimates, guiding targeted public health strategies and addressing biases in traditional models to support more equitable decision-making.

The development of comprehensive benchmarks to assess the performance of algorithms on causal tasks is an important, emerging area. The introduction of two physical ‘causal chamber’ systems serves as a firm step towards future, more reliable benchmarks in the field.

Identifying pleiotropic associations for rare variants in multi-ethnic biobank-scale whole-genome sequencing data poses considerable challenges. This study introduced MultiSTAAR as a scalable and robust multi-trait rare variant analysis framework designed for both coding and noncoding regions by integrating multiple variant functional annotations and leveraging multivariate modeling across diverse phenotypes.

A deep learning algorithm is presented to classify single-particle tracking trajectories into theoretical models of anomalous diffusion and detect if the trajectory is related to a model not originally found within the training dataset.