Large deviations theory

In probability theory, the theory of large deviations concerns the asymptotic behaviour of remote tails of sequences of probability distributions. While some basic ideas of the theory can be traced to Laplace, the formalization started with insurance mathematics, namely ruin theory with Cramér and Lundberg. A unified formalization of large deviation theory was developed in 1966, in a paper by Varadhan. Large deviations theory formalizes the heuristic ideas of concentration of measures and widely generalizes the notion of convergence of probability measures.

Roughly speaking, large deviations theory concerns itself with the exponential decline of the probability measures of certain kinds of extreme or tail events.

Introductory examples

Any large deviation is done in the least unlikely of all the unlikely ways!

An elementary example

Consider a sequence of independent tosses of a fair coin. The possible outcomes could be heads or tails. Let us denote the possible outcome of the i-th trial by

X

i

{\displaystyle X_{i}}

, where we encode head as 1 and tail as 0. Now let

M

N

{\displaystyle M_{N}}

denote the mean value after

N

{\displaystyle N}

trials, namely

M

N

=

1

N

∑

i

=

1

N

X

i

{\displaystyle M_{N}={\frac {1}{N}}\sum _{i=1}^{N}X_{i}}

.