Advanced Bayesian Statistical Inference Methods for Simulation-Based Models in Cell Biology

Many advances in science, for example cell biology, are made through the development of mathematical models. These models allow practitioners to gain new insights, assess treatments and to make predictions. The models are most useful when calibrated to real data, including also uncertainty in parameter estimates. However, realistic models are often stochastic and computationally expensive to simulate. The aim of this project is to harness and develop new state-of-the-art Bayesian simulation-based inference methods, motivated by real scientific applications in biology.

Michael Carr

Queensland University of Technology

Michael Carr is a graduate of a bachelor of Mathematics at Queensland University of Technology (QUT) majoring in: Decision Science and Applied Economics/Finance. He is interested in conducting research into computational Bayesian statistics to solve simulation based models in cell biology. More specifically, developing significantly more computationally efficient and accurate parameter estimation algorithms.

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