Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Employer
- Monash University
- ;
- University of Oslo
- Imperial College London
- Nature Careers
- ; University of Warwick
- ETH Zurich
- Forschungszentrum Jülich
- Heriot Watt University
- Institut Pasteur
- SciLifeLab
- Swedish University of Agricultural Sciences
- University of Glasgow
- ; University of Southampton
- ; University of Sussex
- Argonne
- Arizona State University
- Aston University
- Australian National University
- CEA
- Chalmers University of Technology
- DURHAM UNIVERSITY
- Durham University
- Freenome
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- KINGS COLLEGE LONDON
- King's College London
- La Trobe University
- NIST
- Nanyang Technological University
- Purdue University
- Queensland University of Technology
- SUNY University at Buffalo
- Technical University of Denmark
- University of Adelaide
- University of Birmingham
- University of Bristol
- University of California, Los Angeles
- University of Cambridge
- University of Groningen
- University of London
- University of Manchester
- University of Miami
- University of Minnesota
- University of Oxford
- University of Sheffield
- University of Texas at Arlington
- University of Warsaw
- Western Norway University of Applied Sciences
- 39 more »
- « less
-
Field
-
guiding materials measurement experiments to acclerate learning the synthesis-process-structure-property relationship. Machine learning methods include, but are not limited to, Bayesian inference
-
nodes and chemical bonds as edges. Analysis these networks are important as they may provide AI-based approaches for drug discovery. This project will focus on representing and inferring chemical or
-
back at least as far as 1954 (Dowe, 2008a, sec. 1, pp549-550). Discussion of how to do this using the Bayesian information-theoretic minimum message length (MML) approach (Wallace and Boulton, 1968
-
Methods of balancing model complexity with goodness of fit include Akaike's information criterion (AIC), Schwarz's Bayesian information criterion (BIC), minimum description length (MDL) and minimum
-
the Faculty of Science. We will apply Bayesian approaches such as the information-theoretic minimum message length (MML) principle and other approaches to develop a path towards statistically-optimal algorithms
-
. Among the approaches used will be the Bayesian information-theoretic Minimum Message Length (MML) principle (Wallace and Boulton, 1968; Wallace and Dowe, 1999a; Wallace, 2005) References: Wallace, C.S
-
used will the information-theoretic Bayesian minimum message length (MML) principle. Student cohort PhD, possibly Master’s (Minor Thesis) or Honours URLs/references Chen, Li and Gao, Jiti and Vahid