About Bayesian Intelligence

Overview

Bayesian networks are flexible and intuitive graphical models of systems of variables having unknown and uncertain interrelations. Bayesian Intelligence specialises in modelling with Bayesian networks, eliciting probabilistic information from experts, data mining Bayesian networks, programming interfaces with customer products, and training in Bayesian network technology.

People

• Ann E. Nicholson is an Associate Professor at Monash University who specialises in Bayesian network modelling. She is an expert in dynamic Bayesian networks, planning under uncertainty, user modelling, Bayesian inference methods, and knowledge engineering with Bayesian networks. Email: ann (dot) nicholson (at) bayesian-intelligence (dot) com
  
  
• Kevin B. Korb is a Reader at Monash University who specialises in the theory and practice of causal discovery of Bayesian networkss (aka data mining with BNs), machine learning, evaluation theory, the philosophy of scientific method, and informal logic. Email: kevin.korb (at) bayesian-intelligence.com
  
  
• Owen Woodberry is a PhD student whose thesis investigates the potential for evolutionary artificial life simulations for investigating various biological phenomena. He has a background in modelling ecosystems with Bayesian networks. Email: owen (dot) woodberry (at) bayesian-intelligence (dot) com
  
  
• Lloyd Allison is a researcher in computational data analysis and data mining using MML with applications in DNA sequencing, evolutionary tree reconstruction, and compression. He also has research interests in functional programming, inductive logic programming, and the theory of modelling. Email: lloyd (at) bayesian-intelligence (dot) com
  
  
• Lucas R. Hope is a PhD student whose thesis examines information theoretic measures for analysing causal Bayesian networks and for evaluating machine learning algorithms. He has extensive experience in data mining with Bayesian networks. Email: lucas (dot) r (dot) hope (at) gmail (dot) com

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