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Inference of chromosome selection parameters and missegregation rate in cancer from DNA-sequencing data
Nature
Aneuploidy is frequently observed in cancers and has been linked to poor patient outcome. Analysis of aneuploidy in DNA-sequencing (DNA-seq)...
19 hours ago
How Should We Quantify Uncertainty in Statistical Inference?
Frontiers
An inferential statement is any statement about the parameters, form of the underlying process or future outcomes. An inferential statement, that provides...
1 month ago
Global ranking of the sensitivity of interaction potential contributions within classical molecular dynamics force fields
Nature
Uncertainty quantification (UQ) is rapidly becoming a sine qua non for all forms of computational science out of which actionable outcomes...
2 months ago
Characterization and Valuation of the Uncertainty of Calibrated Parameters in Microsimulation Decision Models
Frontiers
Background: We evaluated the implications of different approaches to characterize the uncertainty of calibrated parameters of microsimulation decision...
5 months ago
Analysis of sloppiness in model simulations: Unveiling parameter uncertainty when mathematical models are fitted to data
Science | AAAS
This work introduces a comprehensive approach to assess the sensitivity of model outputs to changes in parameter values, constrained by the...
22 months ago
Estimating global identifiability using conditional mutual information in a Bayesian framework | Scientific Reports
Nature
A novel information-theoretic approach is proposed to assess the global practical identifiability of Bayesian statistical models.
9 months ago
Evaluating the performance of Bayesian and restricted maximum likelihood estimation for stepped wedge cluster randomized trials with a small number of clusters
BMC Medical Research Methodology
Stepped wedge trials are an appealing and potentially powerful cluster randomized trial design. However, they are frequently implemented...
27 months ago
Automatic differentiation and the optimization of differential equation models in biology
Frontiers
A computational revolution unleashed the power of artificial neural networks. At the heart of that revolution is automatic differentiation, which calculates...
1 month ago
Bayesian inference problem, MCMC and variational inference
Towards Data Science
Bayesian inference is a major problem in statistics that is also encountered in many machine learning methods. For example, Gaussian mixture...
61 months ago
Reverse engineering morphogenesis through Bayesian optimization of physics-based models
Nature
Morphogenetic programs coordinate cell signaling and mechanical interactions to shape organs. In systems and synthetic biology,...
2 months ago