Mathematical models are routinely calibrated to experimental data, with goals ranging from
building predictive models to quantifying parameters that cannot be measured. Whether or …

In this work, we:(i) review likelihood-based inference for parameter estimation and the
construction of confidence regions; and (ii) explore the use of techniques from information …

Abstract Models of stochastic processes are widely used in almost all fields of science.
Theory validation, parameter estimation, and prediction all require model calibration and …

Likelihood profiling is an efficient and powerful frequentist approach for parameter
estimation, uncertainty quantification and practical identifiablity analysis. Unfortunately …

Identifying parameters of computational models from experimental data, or model
calibration, is fundamental for assessing and improving the predictability and reliability of …

Almost all fields of science rely upon statistical inference to estimate unknown parameters in
theoretical and computational models. While the performance of modern computer hardware …

To infer the parameters of mechanistic models with intractable likelihoods, techniques such
as approximate Bayesian computation (ABC) are increasingly being adopted. One of the …

The development of mechanistic models of biological systems is a central part of Systems
Biology. One major challenge in developing these models is the accurate inference of model …

Coral reefs are increasingly subjected to major disturbances threatening the health of
marine ecosystems. Substantial research underway to develop intervention strategies that …

Generating spatial predictions of species distribution is a central task for research and
policy. Currently, correlative species distribution models (cSDMs) are among the most …