  The main goal of the LISA Pathfinder (LPF) mission is to fully characterize
the acceleration noise models and to test key technologies for future
space-based gravitational-wave observatories similar to the eLISA concept. The
data analysis team has developed complex three-dimensional models of the LISA
Technology Package (LTP) experiment on-board LPF. These models are used for
simulations, but more importantly, they will be used for parameter estimation
purposes during flight operations. One of the tasks of the data analysis team
is to identify the physical effects that contribute significantly to the
properties of the instrument noise. A way of approaching this problem is to
recover the essential parameters of a LTP model fitting the data. Thus, we want
to define the simplest model that efficiently explains the observations. To do
so, adopting a Bayesian framework, one has to estimate the so-called Bayes
Factor between two competing models. In our analysis, we use three main
different methods to estimate it: The Reversible Jump Markov Chain Monte Carlo
method, the Schwarz criterion, and the Laplace approximation. They are applied
to simulated LPF experiments where the most probable LTP model that explains
the observations is recovered. The same type of analysis presented in this
paper is expected to be followed during flight operations. Moreover, the
correlation of the output of the aforementioned methods with the design of the
experiment is explored.
