  While non-invasive sampling is more and more commonly used in
capture-recapture (CR) experiments, it carries a higher risk of
misidentifications than direct observations. As a consequence, one must screen
the data to retain only the reliable data before applying a classical CR model.
This procedure is unacceptable when too few data would remain. Models able to
deal with misidentifications have been proposed but are barely used. Three
objectives are pursued in this paper. First, we present the Latent Multinomial
Model of Link et al. (2010) where estimates of the model are obtained from a
Monte Carlo Markov Chain (MCMC). Second we show the impact of the use of an
informative prior over the estimations when the capture rate is low. Finally we
extend the model to the multistate paradigm as an example of its flexibility.
  We showed that, without prior information, with capture rate at 0.2 or lower,
parameters of the model are difficult to estimate i.e. either the MCMC does not
converge or the estimates are biased. In that case, we show that adding an
informative prior on the identification probability solves the identifiability
problem of the model and allow for convergence. It also allows for good quality
estimates of population size, although when the capture rate is 0.1 it
underestimates it of about 10%. A similar approach on the multistate extension
show good quality estimates of the population size and transition probabilities
with a capture rate of 0.3 or more.
