  Monte Carlo is a versatile and frequently used tool in statistical physics
and beyond. Correspondingly, the number of algorithms and variants reported in
the literature is vast, and an overview is not easy to achieve. In this
pedagogical review, we start by presenting the probabilistic concepts which are
at the basis of the Monte Carlo method. From these concepts the relevant free
parameters--which still may be adjusted--are identified. Having identified
these parameters, most of the tangled mass of methods and algorithms in
statistical physics Monte Carlo can be regarded as realizations of merely a
handful of basic strategies which are employed in order to improve convergence
of a Monte Carlo computation. Once the notations introduced are available, many
of the most widely used Monte Carlo methods and algorithms can be formulated in
a few lines. In such a formulation, the core ideas are exposed and possible
generalizations of the methods are less obscured by the details of a particular
algorithm.
