How can one perform Bayesian inference on stochastic simulators with intractable likelihoods?
A recent approach is to learn the posterior from adaptively proposed simulations using …

Mechanistic models of single-neuron dynamics have been extensively studied in
computational neuroscience. However, identifying which models can quantitatively reproduce …

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of
underlying causes. However, determining which model parameters agree with complex and …

To understand and predict large, complex, and chaotic systems, Earth scientists build
simulators from physical laws. Simulators generalize better to new scenarios, require fewer …

The rise of large-scale recordings of neuronal activity has fueled the hope to gain new insights
into the collective activity of neural ensembles. How can one link the statistics of neural …

A powerful approach for understanding neural population dynamics is to extract low-dimensional
trajectories from population recordings using dimensionality reduction methods. …

Statistical climate prediction has sometimes demonstrated higher accuracy than coupled
dynamical forecast systems. This study tests the applicability of springtime soil moisture (SM) …

The von Mises-Fisher distribution as an exponential family can be expressed in terms of either
its natural or its mean parameters. Unfortunately, however, the normalization function for …

Large-scale recordings of neuronal activity make it possible to gain insights into the collective
activity of neural ensembles. It has been hypothesized that neural populations might be …

Neural networks can learn local interactions to faithfully reproduce large-scale dynamics in
important physical systems. Trained on PDE integrations or noisy observations, these …