  Soft tissues - such as ligaments and tendons - primarily consist of solid
(collagen, predominantly) and liquid phases. Understanding the interaction
between such components and how they change under physiological loading sets
the basis for elucidating the essential link between their internal structure
and mechanical behaviour. In fact, the internal heterogeneous structure of this
kind of tissues leads to a wide range of mechanical behaviours, which then
determine their own function(s). Characterising these behaviours implies an
important experimental effort in terms of tissue harvesting, samples
preparation and implementation of testing protocols - which, often, are not
standardised. These issues lead to several difficulties in both collecting and
providing comparable and reliable information. In order to model the behaviours
of heterogeneous tissues and identify material parameters, a large volume of
reproducible experimental data is required; unfortunately, such an amount of
information is often not available. In reality, most of the studies that are
focused on the identification of material parameters, are largely based on
small sets of experimental data, which present a large variability. Such a
large variability opens on to uncertainties in the estimation of material
parameters, as reported in the literature. Hence, the use of a rigorous
probabilistic framework, that is able to address uncertainties due to paucity
of data, is of paramount importance in the field of biomechanics; in this
perspective, Bayesian inference represents a promising approach. This study was
focused on the analysis of the knee meniscus as a paradigmatic example of human
soft tissue.
