Many of the statistical models that could provide an accurate, interesting, and testable
explanation for the structure of a data set turn out to have intractable likelihood functions …

Abstract Model calibration is crucial for optimizing the performance of complex computer
models across various disciplines. In the era of Industry 4.0, symbolizing rapid technological …

Abstract Approximate Bayesian Computation (ABC) techniques are a suite of model fitting
methods which can be implemented without a using likelihood function. In order to use ABC …

The estimation of parameters and model structure for informing infectious disease response
has become a focal point of the recent pandemic. However, it has also highlighted a …

We introduce June, an open-source framework for the detailed simulation of epidemics on
the basis of social interactions in a virtual population constructed from geographically …

Analyzing Stochastic Computer Models: A Review with Opportunities Page 1 Statistical
Science 2022, Vol. 37, No. 1, 64–89 https://doi.org/10.1214/21-STS822 © Institute of …

There is an inherent tension in Quantitative Systems Pharmacology (QSP) between the
need to incorporate mathematical descriptions of complex physiology and drug targets with …

Individual-based models (IBMs) informing public health policy should be calibrated to data
and provide estimates of uncertainty. Two main components of model-calibration methods …

We analyze JUNE: a detailed model of COVID-19 transmission with high spatial and
demographic resolution, developed as part of the RAMP initiative. JUNE requires substantial …

Background Many mathematical models have now been employed across every area of
systems biology. These models increasingly involve large numbers of unknown parameters …