  Large deviation theory is a branch of probability theory that is devoted to a
study of the "rate" at which empirical estimates of various quantities converge
to their true values. The object of study in this paper is the rate at which
estimates of the doublet frequencies of a Markov chain over a finite alphabet
converge to their true values. In case the Markov process is actually an
i.i.d.\ process, the rate function turns out to be the relative entropy (or
Kullback-Leibler divergence) between the true and the estimated probability
vectors. This result is a special case of a very general result known as
Sanov's theorem and dates back to 1957. Moreover, since the introduction of the
"method of types" by Csisz\'{a}r and his co-workers during the 1980s, the proof
of this version of Sanov's theorem has been "elementary," using some
combinatorial arguments. However, when the i.i.d.\ process is replaced by a
Markov process, the available proofs are far more complex. The main objective
of this paper is therefore to present a first-principles derivation of the LDP
for finite state Markov chains, using only simple combinatorial arguments
(e.g.\ the method of types), thus gathering in one place various arguments and
estimates that are scattered over the literature.
