  This paper presents a complete methodology for Bayesian inference on a
semi-Markov process, from the elicitation of the prior distribution, to the
computation of posterior summaries, including a guidance for its JAGS
implementation. The holding times (conditional on the transition between two
given states) are assumed to be Weibull-distributed. We examine the elicitation
of the joint prior density of the shape and scale parameters of the Weibull
distributions, deriving a specific class of priors in a natural way, along with
a method for the determination of hyperparameters based on ``learning data''
and moment existence conditions. This framework is applied to data of
earthquakes of three types of severity (low, medium and high size) that
occurred in the central Northern Apennines in Italy and collected by the
\cite{CPTI04} catalogue. Assumptions on two types of energy accumulation and
release mechanisms are evaluated.
