  The aim of this review is to provide a concise overview of some of the
generic approaches that have been developed to deal with the statistical
description of large systems of interacting dissipative 'units'. The latter
notion includes, e.g., inelastic grains, active or self-propelled particles,
bubbles in a foam, low-dimensional dynamical systems like driven oscillators,
or even spatially extended modes like Fourier modes of the velocity field in a
fluid for instance. We first review methods based on the statistical properties
of a single unit, starting with elementary mean-field approximations, either
static or dynamic, that describe a unit embedded in a 'self-consistent'
environment. We then discuss how this basic mean-field approach can be extended
to account for spatial dependences, in the form of space-dependent mean-field
Fokker-Planck equations for example. We also briefly review the use of kinetic
theory in the framework of the Boltzmann equation, which is an appropriate
description for dilute systems. We then turn to descriptions in terms of the
full $N$-body distribution, starting from exact solutions of one-dimensional
models, using a Matrix Product Ansatz method when correlations are present.
Since exactly solvable models are scarce, we also present some approximation
methods that can be used to determine the $N$-body distribution in a large
system of dissipative units. These methods include the Edwards approach for
dense granular matter and the approximate treatment of multiparticle Langevin
equations with coloured noise, which models systems of self-propelled
particles. Throughout this review, emphasis is put on methodological aspects of
the statistical modeling and on formal similarities between different physical
problems, rather than on the specific behaviour of a given system.
