You need to know: Basic probability theory, random variable, distribution of a random variable, independent identically distributed (i.i.d.) random variables, infimum , supremum
, limit superior
.
Background: For , Bernoulli convolution
is the distribution of the real random variable
, where the signs are chosen i.i.d. with equal probabilities. It is known that the limit
exists and is constant for
-almost every x. This constant is called dimension of
and in denoted
. The box dimension of set
of real numbers is
, where
is the minimal number of intervals of length
needed to cover S. The packing dimension of S is
.
The Theorem: On 9th December 2012, Michael Hochman submitted to arxiv a paper in which he proved, among other results, that the set has packing dimension
. In other words,
outside a set of
of packing dimension
.
Short context: Bernoulli convolutions has been introduced by Jessen and Wintner in 1935, and studied by Erdős and many others since that. The main question is for which values of the resulting probability measure
is “nice”, and having dimension 1 is one of the criteria for “niceness”. The Theorem states that the set of exceptional
-s for which
may be “not nice” is very small in a strong sense. For readers familiar with Hausdorff dimension, we note that having packing dimension
implies Hausdorff dimension
but not vice versa.
Links: Free arxiv version of the original paper is here, journal version is here.
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