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Department of Mathematical Sciences

Research Seminar Series

Applied Mathematics Seminars

Statistics Seminars: The Geometry of Sloppiness

Presented by Emilie Dufresne, University of Nottingham

16 October 2017 14:00 in CM221

The use of mathematical models in the sciences often require the estimation of unknown parameter values from data. Sloppiness provides information about the uncertainty of this task. We develop a precise mathematical foundation for sloppiness and define rigorously its key concepts, such as `model manifold' in relation to concepts of structural identifiability. We redefine sloppiness conceptually as a comparison between the premetric on parameter space induced by measurement noise and a reference metric on parameter space. This opens up the possibility of alternative quantification of sloppiness beyond the traditional use of the Fisher Information Matrix, which implicitly assumes infinitesimal measurement error and an Euclidean parameter space. We illustrate the various concepts involved in the proper definition of sloppiness with examples of ordinary differential equation models with time series data arising in mathematical biology.

(joint with Heather Harrington and Dhruva Raman)

Contact sunil.chhita@durham.ac.uk for more information


Arithmetic Study Group

Statistics Seminars: The Geometry of Sloppiness

Presented by Emilie Dufresne, University of Nottingham

16 October 2017 14:00 in CM221

The use of mathematical models in the sciences often require the estimation of unknown parameter values from data. Sloppiness provides information about the uncertainty of this task. We develop a precise mathematical foundation for sloppiness and define rigorously its key concepts, such as `model manifold' in relation to concepts of structural identifiability. We redefine sloppiness conceptually as a comparison between the premetric on parameter space induced by measurement noise and a reference metric on parameter space. This opens up the possibility of alternative quantification of sloppiness beyond the traditional use of the Fisher Information Matrix, which implicitly assumes infinitesimal measurement error and an Euclidean parameter space. We illustrate the various concepts involved in the proper definition of sloppiness with examples of ordinary differential equation models with time series data arising in mathematical biology.

(joint with Heather Harrington and Dhruva Raman)

Contact sunil.chhita@durham.ac.uk for more information


Centre for Particle Theory Colloquia

Statistics Seminars: The Geometry of Sloppiness

Presented by Emilie Dufresne, University of Nottingham

16 October 2017 14:00 in CM221

The use of mathematical models in the sciences often require the estimation of unknown parameter values from data. Sloppiness provides information about the uncertainty of this task. We develop a precise mathematical foundation for sloppiness and define rigorously its key concepts, such as `model manifold' in relation to concepts of structural identifiability. We redefine sloppiness conceptually as a comparison between the premetric on parameter space induced by measurement noise and a reference metric on parameter space. This opens up the possibility of alternative quantification of sloppiness beyond the traditional use of the Fisher Information Matrix, which implicitly assumes infinitesimal measurement error and an Euclidean parameter space. We illustrate the various concepts involved in the proper definition of sloppiness with examples of ordinary differential equation models with time series data arising in mathematical biology.

(joint with Heather Harrington and Dhruva Raman)

Contact sunil.chhita@durham.ac.uk for more information


Computing Seminars/Talks

Statistics Seminars: The Geometry of Sloppiness

Presented by Emilie Dufresne, University of Nottingham

16 October 2017 14:00 in CM221

The use of mathematical models in the sciences often require the estimation of unknown parameter values from data. Sloppiness provides information about the uncertainty of this task. We develop a precise mathematical foundation for sloppiness and define rigorously its key concepts, such as `model manifold' in relation to concepts of structural identifiability. We redefine sloppiness conceptually as a comparison between the premetric on parameter space induced by measurement noise and a reference metric on parameter space. This opens up the possibility of alternative quantification of sloppiness beyond the traditional use of the Fisher Information Matrix, which implicitly assumes infinitesimal measurement error and an Euclidean parameter space. We illustrate the various concepts involved in the proper definition of sloppiness with examples of ordinary differential equation models with time series data arising in mathematical biology.

(joint with Heather Harrington and Dhruva Raman)

Contact sunil.chhita@durham.ac.uk for more information


CPT Student Seminar

Statistics Seminars: The Geometry of Sloppiness

Presented by Emilie Dufresne, University of Nottingham

16 October 2017 14:00 in CM221

The use of mathematical models in the sciences often require the estimation of unknown parameter values from data. Sloppiness provides information about the uncertainty of this task. We develop a precise mathematical foundation for sloppiness and define rigorously its key concepts, such as `model manifold' in relation to concepts of structural identifiability. We redefine sloppiness conceptually as a comparison between the premetric on parameter space induced by measurement noise and a reference metric on parameter space. This opens up the possibility of alternative quantification of sloppiness beyond the traditional use of the Fisher Information Matrix, which implicitly assumes infinitesimal measurement error and an Euclidean parameter space. We illustrate the various concepts involved in the proper definition of sloppiness with examples of ordinary differential equation models with time series data arising in mathematical biology.

(joint with Heather Harrington and Dhruva Raman)

Contact sunil.chhita@durham.ac.uk for more information


Departmental Research Colloquium

Statistics Seminars: The Geometry of Sloppiness

Presented by Emilie Dufresne, University of Nottingham

16 October 2017 14:00 in CM221

The use of mathematical models in the sciences often require the estimation of unknown parameter values from data. Sloppiness provides information about the uncertainty of this task. We develop a precise mathematical foundation for sloppiness and define rigorously its key concepts, such as `model manifold' in relation to concepts of structural identifiability. We redefine sloppiness conceptually as a comparison between the premetric on parameter space induced by measurement noise and a reference metric on parameter space. This opens up the possibility of alternative quantification of sloppiness beyond the traditional use of the Fisher Information Matrix, which implicitly assumes infinitesimal measurement error and an Euclidean parameter space. We illustrate the various concepts involved in the proper definition of sloppiness with examples of ordinary differential equation models with time series data arising in mathematical biology.

(joint with Heather Harrington and Dhruva Raman)

Contact sunil.chhita@durham.ac.uk for more information


Distinguished Lectures and Public Lectures

Statistics Seminars: The Geometry of Sloppiness

Presented by Emilie Dufresne, University of Nottingham

16 October 2017 14:00 in CM221

The use of mathematical models in the sciences often require the estimation of unknown parameter values from data. Sloppiness provides information about the uncertainty of this task. We develop a precise mathematical foundation for sloppiness and define rigorously its key concepts, such as `model manifold' in relation to concepts of structural identifiability. We redefine sloppiness conceptually as a comparison between the premetric on parameter space induced by measurement noise and a reference metric on parameter space. This opens up the possibility of alternative quantification of sloppiness beyond the traditional use of the Fisher Information Matrix, which implicitly assumes infinitesimal measurement error and an Euclidean parameter space. We illustrate the various concepts involved in the proper definition of sloppiness with examples of ordinary differential equation models with time series data arising in mathematical biology.

(joint with Heather Harrington and Dhruva Raman)

Contact sunil.chhita@durham.ac.uk for more information


Geometry and Topology Seminar

Statistics Seminars: The Geometry of Sloppiness

Presented by Emilie Dufresne, University of Nottingham

16 October 2017 14:00 in CM221

The use of mathematical models in the sciences often require the estimation of unknown parameter values from data. Sloppiness provides information about the uncertainty of this task. We develop a precise mathematical foundation for sloppiness and define rigorously its key concepts, such as `model manifold' in relation to concepts of structural identifiability. We redefine sloppiness conceptually as a comparison between the premetric on parameter space induced by measurement noise and a reference metric on parameter space. This opens up the possibility of alternative quantification of sloppiness beyond the traditional use of the Fisher Information Matrix, which implicitly assumes infinitesimal measurement error and an Euclidean parameter space. We illustrate the various concepts involved in the proper definition of sloppiness with examples of ordinary differential equation models with time series data arising in mathematical biology.

(joint with Heather Harrington and Dhruva Raman)

Contact sunil.chhita@durham.ac.uk for more information


Informal HEP Journal club

Statistics Seminars: The Geometry of Sloppiness

Presented by Emilie Dufresne, University of Nottingham

16 October 2017 14:00 in CM221

The use of mathematical models in the sciences often require the estimation of unknown parameter values from data. Sloppiness provides information about the uncertainty of this task. We develop a precise mathematical foundation for sloppiness and define rigorously its key concepts, such as `model manifold' in relation to concepts of structural identifiability. We redefine sloppiness conceptually as a comparison between the premetric on parameter space induced by measurement noise and a reference metric on parameter space. This opens up the possibility of alternative quantification of sloppiness beyond the traditional use of the Fisher Information Matrix, which implicitly assumes infinitesimal measurement error and an Euclidean parameter space. We illustrate the various concepts involved in the proper definition of sloppiness with examples of ordinary differential equation models with time series data arising in mathematical biology.

(joint with Heather Harrington and Dhruva Raman)

Contact sunil.chhita@durham.ac.uk for more information


Maths HEP Lunchtime Seminars

Statistics Seminars: The Geometry of Sloppiness

Presented by Emilie Dufresne, University of Nottingham

16 October 2017 14:00 in CM221

The use of mathematical models in the sciences often require the estimation of unknown parameter values from data. Sloppiness provides information about the uncertainty of this task. We develop a precise mathematical foundation for sloppiness and define rigorously its key concepts, such as `model manifold' in relation to concepts of structural identifiability. We redefine sloppiness conceptually as a comparison between the premetric on parameter space induced by measurement noise and a reference metric on parameter space. This opens up the possibility of alternative quantification of sloppiness beyond the traditional use of the Fisher Information Matrix, which implicitly assumes infinitesimal measurement error and an Euclidean parameter space. We illustrate the various concepts involved in the proper definition of sloppiness with examples of ordinary differential equation models with time series data arising in mathematical biology.

(joint with Heather Harrington and Dhruva Raman)

Contact sunil.chhita@durham.ac.uk for more information


Pure Maths Colloquium

Statistics Seminars: The Geometry of Sloppiness

Presented by Emilie Dufresne, University of Nottingham

16 October 2017 14:00 in CM221

The use of mathematical models in the sciences often require the estimation of unknown parameter values from data. Sloppiness provides information about the uncertainty of this task. We develop a precise mathematical foundation for sloppiness and define rigorously its key concepts, such as `model manifold' in relation to concepts of structural identifiability. We redefine sloppiness conceptually as a comparison between the premetric on parameter space induced by measurement noise and a reference metric on parameter space. This opens up the possibility of alternative quantification of sloppiness beyond the traditional use of the Fisher Information Matrix, which implicitly assumes infinitesimal measurement error and an Euclidean parameter space. We illustrate the various concepts involved in the proper definition of sloppiness with examples of ordinary differential equation models with time series data arising in mathematical biology.

(joint with Heather Harrington and Dhruva Raman)

Contact sunil.chhita@durham.ac.uk for more information


Statistics Seminars

Statistics Seminars: The Geometry of Sloppiness

Presented by Emilie Dufresne, University of Nottingham

16 October 2017 14:00 in CM221

The use of mathematical models in the sciences often require the estimation of unknown parameter values from data. Sloppiness provides information about the uncertainty of this task. We develop a precise mathematical foundation for sloppiness and define rigorously its key concepts, such as `model manifold' in relation to concepts of structural identifiability. We redefine sloppiness conceptually as a comparison between the premetric on parameter space induced by measurement noise and a reference metric on parameter space. This opens up the possibility of alternative quantification of sloppiness beyond the traditional use of the Fisher Information Matrix, which implicitly assumes infinitesimal measurement error and an Euclidean parameter space. We illustrate the various concepts involved in the proper definition of sloppiness with examples of ordinary differential equation models with time series data arising in mathematical biology.

(joint with Heather Harrington and Dhruva Raman)

Contact sunil.chhita@durham.ac.uk for more information


Stats4Grads

Statistics Seminars: The Geometry of Sloppiness

Presented by Emilie Dufresne, University of Nottingham

16 October 2017 14:00 in CM221

The use of mathematical models in the sciences often require the estimation of unknown parameter values from data. Sloppiness provides information about the uncertainty of this task. We develop a precise mathematical foundation for sloppiness and define rigorously its key concepts, such as `model manifold' in relation to concepts of structural identifiability. We redefine sloppiness conceptually as a comparison between the premetric on parameter space induced by measurement noise and a reference metric on parameter space. This opens up the possibility of alternative quantification of sloppiness beyond the traditional use of the Fisher Information Matrix, which implicitly assumes infinitesimal measurement error and an Euclidean parameter space. We illustrate the various concepts involved in the proper definition of sloppiness with examples of ordinary differential equation models with time series data arising in mathematical biology.

(joint with Heather Harrington and Dhruva Raman)

Contact sunil.chhita@durham.ac.uk for more information


Information about seminars for the current academic year. For information on previous years' seminars please see the seminar archives pages.