The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

Abstract We introduce Support Decomposition Variational Inference (SDVI), a new
variational inference (VI) approach for probabilistic programs with stochastic support …

Recent advances in connectomics research enable the acquisition of increasing amounts of
data about the connectivity patterns of neurons. How can we use this wealth of data to …

As the effects of climate change continue to intensify and the world struggles to cut
greenhouse gas emissions fast enough, solar geoengineering solutions are being …

In this work we demonstrate how to automate parts of the infectious disease-control policy-
making process via performing inference in existing epidemiological models. The kind of …

The COVID-19 pandemic has highlighted the importance of in-silico epidemiological
modelling in predicting the dynamics of infectious diseases to inform health policy and …

Probabilistic programming is a paradigm that enables us to efficiently write probabilistic
models as program code that we can sample, infer underlying parameters and predict …

We introduce two approaches for conducting efficient Bayesian inference in stochastic
simulators containing nested stochastic sub-procedures, ie, internal procedures for which …

We demonstrate an approach to replicate and forecast the spread of the SARS-CoV-2
(COVID-19) pandemic using the toolkit of probabilistic programming languages (PPLs). Our …

Complex dynamical systems are found across various scientific disciplines, representing
phenomena like atmospheric and oceanic behavior, brain activity, robot state in its …