Sort by
Refine Your Search
-
Category
-
Country
-
Employer
- University of Oslo
- Montana State University
- UNIVERSITY OF MELBOURNE
- University of Nottingham
- Cambridge, University of
- Center for Biologics Evaluation and Research (CBER)
- King Abdullah University of Science and Technology
- Max Planck Institutes
- Queen's University
- University of Birmingham
- Zintellect
- 1 more »
- « less
-
Field
-
entitled “Beyond Data-Augmentation: Advancing Bayesian Inference for Stochastic Disease Transmission Models”. The overarching aim of the project is to develop the next generation of statistical tools
-
of environmental factors affecting animal health and veterinary public health, including estimates of the likelihood of a damaging event and the resulting consequences. Multidisciplinary teams of specialists use
-
Machine Learning Seminar Group Advanced Tutorial Lecture Series on Machine Learning Non-Parametric Bayes Tutorial Course (October 9, 16 and 28, 2008) Bayesian statistics in other labs Machine Learning and
-
the mismodeling of gravitational waves, of astrophysical environments, or of noise artifacts in gravitational-wave inference, The development of Bayesian data analysis techniques to carry out parameter estimation
-
Center for Biologics Evaluation and Research (CBER) | Silver Spring, Maryland | United States | 10 days ago
, which could include Bayesian methodologies and use of artificial intelligence to systematically integrate available information for evolving benefit-risk assessment. The goal is to tackle the major
-
the mismodeling of gravitational waves, of astrophysical environments, or of noise artifacts in gravitational-wave inference, The development of Bayesian data analysis techniques to carry out parameter estimation
-
uncertainty estimation and (iv) assess the ability to accurately model these complex fluids by using adjoint‑accelerated Bayesian inference with the experimental Flow‑MRI data. Expected Results
-
subcellular mechanisms (proliferation, differentiation) with multicellular mechanical and biochemical interactions. Apply Advanced Statistical Methods: Perform Bayesian parameter estimation and identifiability
-
getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution over the parameter space) without specifying a model nor a prior. Such methods can in principle be applied
-
The Computer Vision-Core Artificial Intelligence Research (Vision-CAIR ) group led by Prof. Mohamed Elhoseiny at the CS Program of the King Abdullah University of Science and Technology (KAUST) is