Mathematical and Statistical Challenges in Bridging Model Development, Parameter Identification and Model Selection in the Biological Sciences
Videos from BIRS Workshop
Darren Wilkinson, Newcastle University
Monday Nov 12, 2018 10:32 - 10:59
Scalable algorithms for Markov process parameter inference
Matthias Chung, Virginia Tech
Monday Nov 12, 2018 11:00 - 11:28
From parameter and uncertainty estimation to optimal experimental design: challenges in biological dynamical systems inference
Adelle Coster, University of New South Wales
Monday Nov 12, 2018 11:31 - 12:01
Building models that encode both the known and the unknown
Gary Mirams, University of Nottingham
Monday Nov 12, 2018 14:03 - 14:34
Challenges in ion channel model calibration, selection and discrepancy
Adam MacLean, University of Southern California
Monday Nov 12, 2018 14:36 - 15:17
Hybrid modeling and parameter inference reveals branching constraints for kidney morphogenesis
Alexander Browning, Queensland University of Technology
Monday Nov 12, 2018 15:32 - 15:59
A Bayesian sequential learning framework to parametrise a model of melanoma invasion into human skin
Michael Plank, University of Canterbury
Monday Nov 12, 2018 16:01 - 16:36
Spatial moment models for collective cell behaviour
Rob Deardon, University of Calgary
Tuesday Nov 13, 2018 09:00 - 09:30
Emulation-based methods for parameterizing spatial infectious disease models
Dennis Prangle, Newcastle University
Tuesday Nov 13, 2018 09:33 - 10:04
Variational inference for stochastic differential equations
David Campbell, Simon Fraser University
Tuesday Nov 13, 2018 10:05 - 10:45
Testing for statistical parameter identifiability
Alexandre Bouchard-Côté, UBC
Tuesday Nov 13, 2018 11:05 - 11:35
Bayesian computational biology
Thomas Prescott, University of Oxford
Tuesday Nov 13, 2018 13:33 - 14:03
Multifidelity approaches to approximate Bayesian computation
Ramon Grima, University of Edinburgh
Tuesday Nov 13, 2018 14:07 - 14:35
Computationally efficient parameter estimation for gene regulatory networks
Simon Cotter, University of Manchester
Tuesday Nov 13, 2018 14:36 - 15:10
Transport map-accelerated adaptive importance sampling for inverse problems of multiscale stochastic chemical networks
Priscilla (Cindy) Greenwood, University of British Colombia
Tuesday Nov 13, 2018 16:08 - 16:38
Stochastic vs. deterministic modeling in bio-science
Jonathan Dushoff, McMaster U
Wednesday Nov 14, 2018 09:01 - 09:30
Bridging between statistics and science: Some philosophical claptrap
Mark Lewis, University of Victoria
Wednesday Nov 14, 2018 09:31 - 10:01
Study design and parameter estimability for spatial and temporal ecological models using data cloning
Aaron King, University of Michigan
Wednesday Nov 14, 2018 10:02 - 10:34
Forward-in-time phylodynamics via sequential Monte Carlo
Oksana Chkrebtii, The Ohio State University
Wednesday Nov 14, 2018 11:04 - 11:34
Identifying individual disease dynamics in a stochastic multi-pathogen model from aggregated reports and laboratory data
Mike Dowd, Dalhousie University
Thursday Nov 15, 2018 09:02 - 09:35
Sequential Monte Carlo approaches for inference in dynamical systems: application to spatio-temporal models of ocean biogeochemistry
Oliver Maclaren, University of Auckland
Thursday Nov 15, 2018 09:37 - 10:11
Lessons for biological parameter estimation from large-scale engineering inverse problems
Barbara Holland, University of Tasmania
Thursday Nov 15, 2018 10:38 - 11:10
Assessing model adequacy in molecular phylogenetics
Paul Francois, McGill University
Thursday Nov 15, 2018 11:11 - 11:52
Untangling the hairball: fitness based reduction of biological networks
Jill Gallaher, Moffitt Cancer Center
Thursday Nov 15, 2018 13:31 - 14:01
Systemic dynamics and effects from multiple metastases during adaptive therapy in prostate cancer
Susanna Röblitz, University of Bergen
Thursday Nov 15, 2018 14:03 - 14:36
Empirical Bayes methods for prior estimation in systems biology modelling
Jonathan Harrison, University of Oxford
Thursday Nov 15, 2018 15:00 - 15:32
Experimental verification of a coarse-grained model predicts that production is rate-limiting for mRNA localization
John Fricks, Arizona State University
Thursday Nov 15, 2018 15:33 - 16:05
Estimating velocity from time traces of molecular motors
