Research at Bayesian Intelligence

• Knowledge Engineering means eliciting information from human experts to build representations useful for modelling systems or problems of interest. In addition to simply consulting with experts, knowledge engineers have access to a wide variety of specialised techniques, such as
  
  
  • computer programs to assist with eliciting probabilities,
  • constraint optimisation programs for finding the closest probabilities to those elicited which are consistent,
  • programs for performing sensitivity analysis on Bayesian networks to help validate or correct elicited probabilities and causal structure,
  • measures and programs for evaluating Bayesian networks using empirical data,
  • methodologies for Knowledge Engineering Bayesian Networks (KEBN).
  
  
  We conduct research into all of these issues.
  
  
• Data Mining is the use of computational techniques for discovering patterns in large volumes of data, which used to be known as machine learning. There are a large number of types of representation of such patterns, including:
  
  
  • probability distributions
  • mixtures of probability distributions
  • sequences of distributions (e.g., for time series analysis)
  • classification trees and graphs
  • Bayesian networks
  • naive Bayesian networks (simplified, for prediction problems)
  • dynamic Bayesian networks (complex BNs for time series or planning)
  
  
  We have a research history with all of these forms of representation and using a variety of data mining techniques, including: causal discovery search algorithms (CaMML, PC, GES, K2); genetic algorithms for search; MML, orthodox, and other methods for measuring the value of a representation. We also have special expertise in the evaluation of data mining techniques.