Welcome
Probability Theory
Probability Theory Fundamentals: About this section
Frequentist approach to probability
Bayesian approach to probability
Probability axioms
Variables and probability distributions
Joint events and marginalisation
Conditional probability
Bayes rule
Bayes Rule Example
Likelihood Ratio
Chain rule
Independence and conditional independence
Biases and fallacies in reasoning about probability
Biases and fallacies in reasoning about probability: about this section
Representativeness
Denial of Uncertainty
Availability
Adjustment of uncertainty
Conjunction Fallacy
Hindsight bias
Conservatism
Overconfidence
Fallacies associated with causal and diagnostic reasoning
What is a
Bayesian network?
What is a
Bayesian network?
BBNs: a detailed account
BBN Tutorial: About this section
Definition of BBNs: graphs and probability tables
Analysing a BBN: entering evidence and propagation
The notion of 'explaining away' evidence
Why do we need a BBN for the probability computations?
General case of joint probability distribution in BBN
What happens when the number of variables increases?
In summary: why should we use BBNs?
How BBNs deal with evidence: about this section.
Serial Connection
Diverging connection
Converging connection
The notion of d-separation
BBNs and Bayesian Probability Resources
BBNs and Bayesian Probability Resources: Overview
Probability References
References on eliciting probabilities
Books about BBNs
Papers about BBNs
BBN related web sites
BBN tools
Relevant Journals for BBNs
People working on BBNs