168 research outputs found
Étude spectrale minutieuse de processus moins indécis que les autres
International audienceIn this paper we are looking for quantitative estimates for the convergene to equilibrium of non reversible Markov processes, especialy in short times. The models studied are simple enough to get an explicit expression of the L2 distance betweeen the semigroup and the invariant measure throught time and to compare it with the corresponding reversible cases
Translated Poisson approximation to equilibrium distributions of Markov population processes
The paper is concerned with the equilibrium distributions of continuous-time
density dependent Markov processes on the integers. These distributions are
known typically to be approximately normal, and the approximation error, as
measured in Kolmogorov distance, is of the smallest order that is compatible
with their having integer support. Here, an approximation in the much stronger
total variation norm is established, without any loss in the asymptotic order
of accuracy; the approximating distribution is a translated Poisson
distribution having the same variance and (almost) the same mean. Our arguments
are based on the Stein-Chen method and Dynkin's formula.Comment: 18 page
On Exceptional Times for generalized Fleming-Viot Processes with Mutations
If is a standard Fleming-Viot process with constant mutation rate
(in the infinitely many sites model) then it is well known that for each
the measure is purely atomic with infinitely many atoms. However,
Schmuland proved that there is a critical value for the mutation rate under
which almost surely there are exceptional times at which is a
finite sum of weighted Dirac masses. In the present work we discuss the
existence of such exceptional times for the generalized Fleming-Viot processes.
In the case of Beta-Fleming-Viot processes with index we
show that - irrespectively of the mutation rate and - the number of
atoms is almost surely always infinite. The proof combines a Pitman-Yor type
representation with a disintegration formula, Lamperti's transformation for
self-similar processes and covering results for Poisson point processes
The Mean Drift: Tailoring the Mean Field Theory of Markov Processes for Real-World Applications
The statement of the mean field approximation theorem in the mean field
theory of Markov processes particularly targets the behaviour of population
processes with an unbounded number of agents. However, in most real-world
engineering applications one faces the problem of analysing middle-sized
systems in which the number of agents is bounded. In this paper we build on
previous work in this area and introduce the mean drift. We present the concept
of population processes and the conditions under which the approximation
theorems apply, and then show how the mean drift is derived through a
systematic application of the propagation of chaos. We then use the mean drift
to construct a new set of ordinary differential equations which address the
analysis of population processes with an arbitrary size
A new approach to quantitative propagation of chaos for drift, diffusion and jump processes
This paper is devoted the the study of the mean field limit for many-particle
systems undergoing jump, drift or diffusion processes, as well as combinations
of them. The main results are quantitative estimates on the decay of
fluctuations around the deterministic limit and of correlations between
particles, as the number of particles goes to infinity. To this end we
introduce a general functional framework which reduces this question to the one
of proving a purely functional estimate on some abstract generator operators
(consistency estimate) together with fine stability estimates on the flow of
the limiting nonlinear equation (stability estimates). Then we apply this
method to a Boltzmann collision jump process (for Maxwell molecules), to a
McKean-Vlasov drift-diffusion process and to an inelastic Boltzmann collision
jump process with (stochastic) thermal bath. To our knowledge, our approach
yields the first such quantitative results for a combination of jump and
diffusion processes.Comment: v2 (55 pages): many improvements on the presentation, v3: correction
of a few typos, to appear In Probability Theory and Related Field
Synthesizing and tuning chemical reaction networks with specified behaviours
We consider how to generate chemical reaction networks (CRNs) from functional
specifications. We propose a two-stage approach that combines synthesis by
satisfiability modulo theories and Markov chain Monte Carlo based optimisation.
First, we identify candidate CRNs that have the possibility to produce correct
computations for a given finite set of inputs. We then optimise the reaction
rates of each CRN using a combination of stochastic search techniques applied
to the chemical master equation, simultaneously improving the of correct
behaviour and ruling out spurious solutions. In addition, we use techniques
from continuous time Markov chain theory to study the expected termination time
for each CRN. We illustrate our approach by identifying CRNs for majority
decision-making and division computation, which includes the identification of
both known and unknown networks.Comment: 17 pages, 6 figures, appeared the proceedings of the 21st conference
on DNA Computing and Molecular Programming, 201
Functional limit theorems for random regular graphs
Consider d uniformly random permutation matrices on n labels. Consider the
sum of these matrices along with their transposes. The total can be interpreted
as the adjacency matrix of a random regular graph of degree 2d on n vertices.
We consider limit theorems for various combinatorial and analytical properties
of this graph (or the matrix) as n grows to infinity, either when d is kept
fixed or grows slowly with n. In a suitable weak convergence framework, we
prove that the (finite but growing in length) sequences of the number of short
cycles and of cyclically non-backtracking walks converge to distributional
limits. We estimate the total variation distance from the limit using Stein's
method. As an application of these results we derive limits of linear
functionals of the eigenvalues of the adjacency matrix. A key step in this
latter derivation is an extension of the Kahn-Szemer\'edi argument for
estimating the second largest eigenvalue for all values of d and n.Comment: Added Remark 27. 39 pages. To appear in Probability Theory and
Related Field
Systemic Risk and Default Clustering for Large Financial Systems
As it is known in the finance risk and macroeconomics literature,
risk-sharing in large portfolios may increase the probability of creation of
default clusters and of systemic risk. We review recent developments on
mathematical and computational tools for the quantification of such phenomena.
Limiting analysis such as law of large numbers and central limit theorems allow
to approximate the distribution in large systems and study quantities such as
the loss distribution in large portfolios. Large deviations analysis allow us
to study the tail of the loss distribution and to identify pathways to default
clustering. Sensitivity analysis allows to understand the most likely ways in
which different effects, such as contagion and systematic risks, combine to
lead to large default rates. Such results could give useful insights into how
to optimally safeguard against such events.Comment: in Large Deviations and Asymptotic Methods in Finance, (Editors: P.
Friz, J. Gatheral, A. Gulisashvili, A. Jacqier, J. Teichmann) , Springer
Proceedings in Mathematics and Statistics, Vol. 110 2015
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