89 research outputs found
On Bernoulli Decompositions for Random Variables, Concentration Bounds, and Spectral Localization
As was noted already by A. N. Kolmogorov, any random variable has a Bernoulli
component. This observation provides a tool for the extension of results which
are known for Bernoulli random variables to arbitrary distributions. Two
applications are provided here: i. an anti-concentration bound for a class of
functions of independent random variables, where probabilistic bounds are
extracted from combinatorial results, and ii. a proof, based on the Bernoulli
case, of spectral localization for random Schroedinger operators with arbitrary
probability distributions for the single site coupling constants. For a general
random variable, the Bernoulli component may be defined so that its conditional
variance is uniformly positive. The natural maximization problem is an optimal
transport question which is also addressed here
Chains of infinite order, chains with memory of variable length, and maps of the interval
We show how to construct a topological Markov map of the interval whose
invariant probability measure is the stationary law of a given stochastic chain
of infinite order. In particular we caracterize the maps corresponding to
stochastic chains with memory of variable length. The problem treated here is
the converse of the classical construction of the Gibbs formalism for Markov
expanding maps of the interval
Exponential Mixing for a Stochastic PDE Driven by Degenerate Noise
We study stochastic partial differential equations of the reaction-diffusion
type. We show that, even if the forcing is very degenerate (i.e. has not full
rank), one has exponential convergence towards the invariant measure. The
convergence takes place in the topology induced by a weighted variation norm
and uses a kind of (uniform) Doeblin condition.Comment: 10 pages, 1 figur
On conformal measures and harmonic functions for group extensions
We prove a Perron-Frobenius-Ruelle theorem for group extensions of
topological Markov chains based on a construction of -finite conformal
measures and give applications to the construction of harmonic functions.Comment: To appear in Proceedings of "New Trends in Onedimensional Dynamics,
celebrating the 70th birthday of Welington de Melo
One-sided versus two-sided stochastic descriptions
It is well-known that discrete-time finite-state Markov Chains, which are
described by one-sided conditional probabilities which describe a dependence on
the past as only dependent on the present, can also be described as
one-dimensional Markov Fields, that is, nearest-neighbour Gibbs measures for
finite-spin models, which are described by two-sided conditional probabilities.
In such Markov Fields the time interpretation of past and future is being
replaced by the space interpretation of an interior volume, surrounded by an
exterior to the left and to the right.
If we relax the Markov requirement to weak dependence, that is, continuous
dependence, either on the past (generalising the Markov-Chain description) or
on the external configuration (generalising the Markov-Field description), it
turns out this equivalence breaks down, and neither class contains the other.
In one direction this result has been known for a few years, in the opposite
direction a counterexample was found recently. Our counterexample is based on
the phenomenon of entropic repulsion in long-range Ising (or "Dyson") models.Comment: 13 pages, Contribution for "Statistical Mechanics of Classical and
Disordered Systems
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