  Simulation models of epidemiological, biological, ecological, and
environmental processes are increasingly being calibrated using Bayesian
statistics. The Bayesian approach provides simple rules to synthesise multiple
data sources and to calculate uncertainty in model output due to uncertainty in
the calibration data. As the number of tutorials and studies published grow,
the solutions to common difficulties in Bayesian calibration across these
fields have become more apparent, and a step-by-step process for successful
calibration across all these fields is emerging. We provide a statement of the
key steps in a Bayesian calibration, and we outline analyses and approaches to
each step that have emerged from one or more of these applied sciences. Thus we
present a synthesis of Bayesian calibration methodologies that cut across a
number of scientific disciplines.
  To demonstrate these steps and to provide further detail on the computations
involved in Bayesian calibration, we calibrated a compartmental model of
tobacco smoking behaviour in Australia. We found that the proportion of a birth
cohort estimated to take up smoking before they reach age 20 years in 2016 was
at its lowest value since the early 20th century, and that quit rates were at
their highest. As a novel outcome, we quantified the rate that ex-smokers
switched to reporting as a 'never smoker' when surveyed later in life; a
phenomenon that, to our knowledge, has never been quantified using
cross-sectional survey data.
