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