## Abstract

This chapter introduces a probabilistic approach to modelling in physiology and medicine: the quantities of interest are modeled as random variables and the focus is on the probabilistic dependencies between these variables. As primary tool in this modelling framework, we present Bayesian networks (BNs), which map the dependencies between a set of random variables to a directed acyclic graph, both increasing human readability and simplifying the representation of the joint probability distribution of the set of variables. The chapter first describes the theoretical foundations of BNs, including a brief review of probability and graph theory, a formal definition of BNs and details on discrete, continuous, and dynamic BNs. Then, a selection of algorithms for inference, conditional probability learning, and structure learning is presented. Finally, several examples of BN applications in biomedicine are reviewed.

Original language | English |
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Title of host publication | Modeling Methodology for Physiology and Medicine: Second Edition |

Publisher | Elsevier Inc. |

Pages | 257-280 |

Number of pages | 24 |

ISBN (Print) | 9780124115576 |

DOIs | |

Publication status | Published - Dec 2013 |

## Keywords

- Bayesian networks
- Conditional probability estimation
- Graphical models
- Probabilistic inference
- Probabilistic modelling
- Structure learning

## ASJC Scopus subject areas

- Engineering(all)