Consulting Services

We provide a wide range of consulting services in the use of Bayesian network technology:

  • Knowledge Engineering. We can help clients build Bayesian network models of their problem domains. We can work with domain experts to help articulate their understanding of a problem or system showing aspects of uncertainty. We have software tools available for simplifying and validating the elicitation of causal relationships, probabilistic dependencies and probabilities.

  • Data Mining. Causal discovery algorithms can automatically find the best Bayesian networks for explaining sample data (e.g., data bases of information) when the variables sampled are related to each other directly or indirectly. These algorithms can be tuned using the results of knowledge engineering with experts to find useful solutions more rapidly. We also have experience with many other data mining methods, including predictive Bayesian networks, classification trees and graphs, and time series analysis.

  • Training. We offer training in all of the Bayesian network techniques we use, ranging from introductory overviews, to in-depth theoretical treatments or to hands-on application training. We also offer training in the use of Netica.

  • Programming. We can tailor tools to particular software environments or program stand-alone solutions.

  • Solutions. We can package any of these services together in whatever combination you need. Ongoing maintenance, including support and enhancement, is negotiable.

Research at Bayesian Intelligence

Bayesian network technology is rapidly growing, both in the underlying technology itself and in applications throughout the community. It is necessary to maintain connection with continuing research in the technology to deliver the best possible services in their use. People at Bayesian Intelligence are actively engaged in relevant research, and espcially in:
  • 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.