The Parameter Houlihan: a solution to high-throughput identifiability indeterminacy for brutally ill-posed problems.

[1]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[2]  O. William.  Journal Of The American Statistical Association V-28 , 1932 .

[3]  H. Hotelling.  The most predictable criterion. , 1935 .

[4]  E. Hill.  Journal of Theoretical Biology , 1961, Nature.

[5]  A. Jazwinski.  Stochastic Processes and Filtering Theory , 1970 .

[6]  Y. Sinai.  GIBBS MEASURES IN ERGODIC THEORY , 1972 .

[7]  V. Arnold,et al.  Ordinary Differential Equations , 1973 .

[8]  R. Bowen.  Equilibrium States and the Ergodic Theory of Anosov Diffeomorphisms , 1975 .

[9]  David Ruelle,et al.  A MEASURE ASSOCIATED WITH AXIOM-A ATTRACTORS. , 1976 .

[10]  L. Glass,et al.  Oscillation and chaos in physiological control systems. , 1977, Science.

[11]  S. Newhouse.  STRUCTURAL STABILITY AND BIFURCATION THEORY , 1979 .

[12]  V L Gott,et al.  Termination of malignant ventricular arrhythmias with an implanted automatic defibrillator in human beings. , 1980, The New England journal of medicine.

[13]  Vladimir Igorevich Arnold,et al.  Geometrical Methods in the Theory of Ordinary Differential Equations , 1983 .

[14]  Andrew C. Lorenc,et al.  Analysis methods for numerical weather prediction , 1986 .

[15]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[16]  C. M. Place,et al.  An Introduction to Dynamical Systems , 1990 .

[17]  Kurt Hornik,et al.  Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks , 1990, Neural Networks.

[18]  E. Mosekilde,et al.  Computer model for mechanisms underlying ultradian oscillations of insulin and glucose. , 1991, The American journal of physiology.

[19]  Max Donath,et al.  American Control Conference , 1993 .

[20]  L. Breiman.  Better subset regression using the nonnegative garrote , 1995 .

[21]  H.F. Durrant-Whyte,et al.  A new approach for filtering nonlinear systems , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[22]  L. Glass,et al.  DYNAMIC CONTROL OF CARDIAC ALTERNANS , 1997 .

[23]  T. Yildirim,et al.  Unusual Structure, Phase Transition, and Dynamics of Solid Cubane , 1997 .

[24]  Rudolph van der Merwe,et al.  Dual Estimation and the Unscented Transformation , 1999, NIPS.

[25]  Adrian E. Raftery,et al.  Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .

[26]  Thomas de Quincey.  [C] , 2000, The Works of Thomas De Quincey, Vol. 1: Writings, 1799–1820.

[27]  Simon Haykin,et al.  Adaptive Systems for Signal Process , 2000 .

[28]  Francis J. Doyle,et al.  Robust H∞ glucose control in diabetes using a physiological model , 2000 .

[29]  R S Parker,et al.  The intravenous route to blood glucose control. , 2001, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[30]  Nils Lid Hjort,et al.  Model Selection and Model Averaging , 2001 .

[31]  David J. Christini,et al.  Introduction: Mapping and control of complex cardiac arrhythmias. , 2002, Chaos.

[32]  Lai-Sang Young,et al.  What Are SRB Measures, and Which Dynamical Systems Have Them? , 2002 .

[33]  Garrett Stuck,et al.  Introduction to Dynamical Systems , 2003 .

[34]  E Massad,et al.  Vaccination against rubella: analysis of the temporal evolution of the age-dependent force of infection and the effects of different contact patterns. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[35]  A. ADoefaa,et al.  ? ? ? ? f ? ? ? ? ? , 2003 .

[36]  David T. Westwick,et al.  Identification of nonlinear physiological systems , 2003 .

[37]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[38]  D. J. Albers,et al.  Structural stability and hyperbolicity violation in high-dimensional dynamical systems , 2004 .

[39]  C. Hann,et al.  Adaptive Bolus-Based Set-Point Regulation of Hyperglycemia in Critical Care , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[40]  A. Raftery,et al.  Using Bayesian Model Averaging to Calibrate Forecast Ensembles , 2005 .

[41]  On the ergodicity of partially hyperbolic systems , 2005, math/0510234.

[42]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[43]  H. Madsen,et al.  Using Stochastic Differential Equations for PK/PD Model Development , 2005, Journal of Pharmacokinetics and Pharmacodynamics.

[44]  J. Gove,et al.  Application of a dual unscented Kalman filter for simultaneous state and parameter estimation in problems of surface‐atmosphere exchange , 2006 .

[45]  J C Sprott,et al.  Persistent chaos in high dimensions. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[46]  G. Evensen.  Data Assimilation: The Ensemble Kalman Filter , 2006 .

[47]  Thomas M. Hamill,et al.  Predictability of Weather and Climate: Ensemble-based atmospheric data assimilation , 2006 .

[48]  D. Nychka.  Data Assimilation” , 2006 .

[49]  H. Musoff,et al.  Unscented Kalman Filter , 2015 .

[50]  Pier Giorgio Fabietti,et al.  Clinical validation of a new control-oriented model of insulin and glucose dynamics in subjects with type 1 diabetes. , 2007, Diabetes technology & therapeutics.

[51]  M. Morari,et al.  Closed-Loop Control of Blood Glucose , 2007 .

[52]  Gilles Clermont,et al.  From Inverse Problems in Mathematical Physiology to Quantitative Differential Diagnoses , 2007, PLoS Comput. Biol..

[53]  Lai-Sang Young,et al.  Shear-induced chaos , 2007, 0705.3294.

[54]  Peter L. Bonate,et al.  Recommended reading in population pharmacokinetic pharmacodynamics , 2005, The AAPS Journal.

[55]  Giovanni Sparacino,et al.  Diabetes: Models, Signals, and Control , 2009 .

[56]  P. Glass,et al.  Pharmacokinetic–pharmacodynamic modeling in anesthesia, intensive care and pain medicine , 2009, Current opinion in anaesthesiology.

[57]  James V. Candy,et al.  Bayesian Signal Processing: Classical, Modern and Particle Filtering Methods , 2009 .

[58]  C. Cobelli,et al.  In Silico Preclinical Trials: A Proof of Concept in Closed-Loop Control of Type 1 Diabetes , 2009, Journal of diabetes science and technology.

[59]  J. F. Selgrade,et al.  A model for hormonal control of the menstrual cycle: structural consistency but sensitivity with regard to data. , 2009, Journal of Theoretical Biology.

[60]  Kazuyuki Aihara,et al.  Development of a mathematical model that predicts the outcome of hormone therapy for prostate cancer. , 2010, Journal of theoretical biology.

[61]  Kazuyuki Aihara,et al.  Hybrid optimal scheduling for intermittent androgen suppression of prostate cancer. , 2010, Chaos.

[62]  Andrew M. Stuart,et al.  Inverse problems: A Bayesian perspective , 2010, Acta Numerica.

[63]  Christopher E. Hann,et al.  A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients , 2011, Comput. Methods Programs Biomed..

[64]  George Hripcsak,et al.  Exploiting time in electronic health record correlations , 2011, J. Am. Medical Informatics Assoc..

[65]  Xiaohua Xia,et al.  On Identifiability of Nonlinear ODE Models and Applications in Viral Dynamics , 2011, SIAM Rev..

[66]  Steven J. Schiff,et al.  Neural Control Engineering: The Emerging Intersection Between Control Theory and Neuroscience , 2011 .

[67]  J. Banga,et al.  Structural Identifiability of Systems Biology Models: A Critical Comparison of Methods , 2011, PloS one.

[68]  Bruce J. Gluckman,et al.  Data assimilation of glucose dynamics for use in the intensive care unit , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[69]  G. Hripcsak,et al.  Population Physiology: Leveraging Electronic Health Record Data to Understand Human Endocrine Dynamics , 2011, PloS one.

[70]  Bruce J. Gluckman,et al.  Reconstructing Mammalian Sleep Dynamics with Data Assimilation , 2012, PLoS Comput. Biol..

[71]  Nicholas G. Polson,et al.  Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model , 2012, Journal of the American Statistical Association.

[72]  Gabriele B. Durrant,et al.  Journal of the Royal Statistical Society Series A (Statistics in Society). Special Issue on Paradata , 2013 .

[73]  G. Hripcsak,et al.  Correlating electronic health record concepts with healthcare process events , 2013, Journal of the American Medical Informatics Association : JAMIA.

[74]  Roman Hovorka,et al.  Feasibility of fully automated closed-loop glucose control using continuous subcutaneous glucose measurements in critical illness: a randomized controlled trial , 2013, Critical Care.

[75]  J. Pathak,et al.  Electronic health records-driven phenotyping: challenges, recent advances, and perspectives. , 2013, Journal of the American Medical Informatics Association : JAMIA.

[76]  G. Roberts,et al.  MCMC Methods for Functions: ModifyingOld Algorithms to Make Them Faster , 2012, 1202.0709.

[77]  Sophie Donnet,et al.  A review on estimation of stochastic differential equations for pharmacokinetic/pharmacodynamic models. , 2013, Advanced drug delivery reviews.

[78]  Martin Jaggi,et al.  An Equivalence between the Lasso and Support Vector Machines , 2013, ArXiv.

[79]  Cédric Jamet,et al.  Data Assimilation Methods , 2013 .

[80]  George Hripcsak,et al.  Next-generation phenotyping of electronic health records , 2012, J. Am. Medical Informatics Assoc..

[81]  Marisa C Eisenberg,et al.  Determining identifiable parameter combinations using subset profiling. , 2014, Mathematical biosciences.

[82]  I. Jolliffe.  Principal Component Analysis , 2005 .

[83]  E. Tabak,et al.  Dynamical Phenotyping: Using Temporal Analysis of Clinically Collected Physiologic Data to Stratify Populations , 2014, PloS one.

[84]  Harvey Thomas Banks,et al.  Modeling and Inverse Problems in the Presence of Uncertainty , 2014 .

[85]  Keegan E. Hines,et al.  Determination of parameter identifiability in nonlinear biophysical models: A Bayesian approach , 2014, The Journal of general physiology.

[86]  Trevor Hastie,et al.  Statistical Learning with Sparsity: The Lasso and Generalizations , 2015 .

[87]  Margaret Nichols.  Trans , 2015, De-centering queer theory.

[88]  Roman Hovorka,et al.  Home Use of an Artificial Beta Cell in Type 1 Diabetes. , 2015, The New England journal of medicine.

[89]  Adler J. Perotte,et al.  Learning probabilistic phenotypes from heterogeneous EHR data , 2015, J. Biomed. Informatics.

[90]  Adler J. Perotte,et al.  Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis , 2015, J. Am. Medical Informatics Assoc..

[91]  Colin J. Cotter,et al.  Probabilistic Forecasting and Bayesian Data Assimilation , 2015 .

[92]  Rik Pintelon,et al.  Linear System Identification in a Nonlinear Setting: Nonparametric Analysis of the Nonlinear Distortions and Their Impact on the Best Linear Approximation , 2016, IEEE Control Systems.

[93]  Adler J. Perotte,et al.  Deep Survival Analysis , 2016, MLHC.

[94]  David Sontag,et al.  Electronic medical record phenotyping using the anchor and learn framework , 2016, J. Am. Medical Informatics Assoc..

[95]  George Hripcsak,et al.  Comparing Lagged Linear Correlation, Lagged Regression, Granger Causality, and Vector Autoregression for Uncovering Associations in EHR Data , 2016, AMIA.

[96]  Adrian E. Raftery,et al.  Bayesian Model Averaging: A Tutorial , 2016 .

[97]  Marisa C. Eisenberg,et al.  Parameter identifiability and identifiable combinations in generalized Hodgkin-Huxley models , 2015, Neurocomputing.

[98]  Marc Bocquet,et al.  Data Assimilation: Methods, Algorithms, and Applications , 2016 .

[99]  Suchi Saria,et al.  A Non-parametric Bayesian Approach for Estimating Treatment-Response Curves from Sparse Time Series , 2016, MLHC.

[100]  Lena Mamykina,et al.  Data-driven health management: reasoning about personally generated data in diabetes with information technologies , 2016, J. Am. Medical Informatics Assoc..

[101]  Lena Mamykina,et al.  Personalized glucose forecasting for type 2 diabetes using data assimilation , 2017, PLoS Comput. Biol..

[102]  Jacqueline L. Simens,et al.  Structural Identifiability Analysis of a Labeled Oral Minimal Model for Quantifying Hepatic Insulin Resistance , 2017 .

[103]  Rafael Meza,et al.  A Systematic Approach to Determining the Identifiability of Multistage Carcinogenesis Models , 2017, Risk analysis : an official publication of the Society for Risk Analysis.

[104]  R. Sarpong,et al.  Bio-inspired synthesis of xishacorenes A, B, and C, and a new congener from fuscol† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c9sc02572c , 2019, Chemical science.

[105]  George Hripcsak,et al.  High-fidelity phenotyping: richness and freedom from bias , 2017, J. Am. Medical Informatics Assoc..

[106]  George Hripcsak,et al.  Methodological variations in lagged regression for detecting physiologic drug effects in EHR data , 2018, J. Biomed. Informatics.

[107]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[108]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[109]  P. Alam.  ‘G’ , 2021, Composites Engineering: An A–Z Guide.

[110]  P. Alam,et al.  H , 1887, High Explosives, Propellants, Pyrotechnics.

[111]  P. Alam.  ‘L’ , 2021, Composites Engineering: An A–Z Guide.

[112]  友紀子 中川.  SoC , 2021, Journal of Japan Society for Fuzzy Theory and Intelligent Informatics.

[113]  P. Alam.  ‘S’ , 2021, Composites Engineering: An A–Z Guide.

[114]  P. Alam,et al.  R , 1823, The Herodotus Encyclopedia.