  1 Sharp prediction of extinction times is needed in biodiversity monitoring
and conservation management. 2 The Galton-Watson process is a classical
stochastic model for describing population dynamics. Its evolution is like the
matrix population model where offspring numbers are random. Extinction
probability, extinction time, abundance are well known and given by explicit
formulas. In contrast with the deterministic model, it can be applied to small
populations. 3 Parameters of this model can be estimated through the Bayesian
inference framework. This enables to consider non-arbitrary scenarios. 4 We
show how coupling Bayesian inference with the Galton-Watson model provides
several features: i) a flexible modelling approach with easily understandable
parameters ii) compatibility with the classical matrix population model (Leslie
type model) iii) A non-computational approach which then leads to more
information with less computing iv) a non-arbitrary choice for scenarios,
parameters... It can be seen to go one step further than the classical matrix
population model for the viability problem. 5 To illustrate these features, we
provide analysis details for two examples whose one of which is a real life
example.

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