Diabetes: models, signals, and control
The control of diabetes is an interdisciplinary endeavor, which includes a significant
biomedical engineering component, with traditions of success beginning in the early 1960s …
biomedical engineering component, with traditions of success beginning in the early 1960s …
Mathematical models and software tools for the glucose-insulin regulatory system and diabetes: an overview
An overview of some of the mathematical models appearing in the literature for use in the
glucose-insulin regulatory system in relation to diabetes is given, enhanced with a survey on …
glucose-insulin regulatory system in relation to diabetes is given, enhanced with a survey on …
Benchmarking simulation-based inference
Recent advances in probabilistic modelling have led to a large number of simulation-based
inference algorithms which do not require numerical evaluation of likelihoods. However, a …
inference algorithms which do not require numerical evaluation of likelihoods. However, a …
Kernel methods in system identification, machine learning and function estimation: A survey
Most of the currently used techniques for linear system identification are based on classical
estimation paradigms coming from mathematical statistics. In particular, maximum likelihood …
estimation paradigms coming from mathematical statistics. In particular, maximum likelihood …
Training deep neural density estimators to identify mechanistic models of neural dynamics
Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of
underlying causes. However, determining which model parameters agree with complex and …
underlying causes. However, determining which model parameters agree with complex and …
Flexible statistical inference for mechanistic models of neural dynamics
JM Lueckmann, PJ Goncalves… - Advances in neural …, 2017 - proceedings.neurips.cc
Mechanistic models of single-neuron dynamics have been extensively studied in
computational neuroscience. However, identifying which models can quantitatively …
computational neuroscience. However, identifying which models can quantitatively …
System identification: A machine learning perspective
A Chiuso, G Pillonetto - Annual Review of Control, Robotics, and …, 2019 - annualreviews.org
Estimation of functions from sparse and noisy data is a central theme in machine learning. In
the last few years, many algorithms have been developed that exploit Tikhonov …
the last few years, many algorithms have been developed that exploit Tikhonov …
The oral minimal model method
The simultaneous assessment of insulin action, secretion, and hepatic extraction is key to
understanding postprandial glucose metabolism in nondiabetic and diabetic humans. We …
understanding postprandial glucose metabolism in nondiabetic and diabetic humans. We …
A unified filter for simultaneous input and state estimation of linear discrete-time stochastic systems
In this paper, we present a unified optimal and exponentially stable filter for linear discrete-
time stochastic systems that simultaneously estimates the states and unknown inputs in an …
time stochastic systems that simultaneously estimates the states and unknown inputs in an …
Calibration of minimally invasive continuous glucose monitoring sensors: state-of-the-art and current perspectives
Minimally invasive continuous glucose monitoring (CGM) sensors are wearable medical
devices that provide real-time measurement of subcutaneous glucose concentration. This …
devices that provide real-time measurement of subcutaneous glucose concentration. This …