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This paper proposes a new general approach based on Bayesian networks tomodel the human behaviour. This approach represents human behaviour withprobabilistic cause-effect relations based on knowledge, but also withconditional probabilities coming either from knowledge or deduced fromobservations. This approach has been applied to the co-simulation of the CO2concentration in an office coupled with human behaviour.

Dynamic Bayesian Networks to simulate occupant behaviours in office buildings related to indoor air quality