Mathematics

Practical parameter identifiability and handling of censored data with Bayesian inference in mathematical tumour models

J. Porthiyas, D. Nussey, et al.

This paper, conducted by Jamie Porthiyas, Daniel Nussey, Catherine A. A. Beauchemin, Donald C. Warren, Christian Quirouette, and Kathleen P. Wilkie, uncovers the pivotal role of decision-making in parameter estimation from experimental tumor growth data, proposing a framework that handles censored data to enhance analysis accuracy.

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