157 research outputs found
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Approximating a diffusion by a finite-state hidden Markov model
© 2016 Elsevier B.V. For a wide class of continuous-time Markov processes evolving on an open, connected subset of Rd, the following are shown to be equivalent: (i) The process satisfies (a slightly weaker version of) the classical Donsker–Varadhan conditions;(ii) The transition semigroup of the process can be approximated by a finite-state hidden Markov model, in a strong sense in terms of an associated operator norm;(iii) The resolvent kernel of the process is ‘v-separable’, that is, it can be approximated arbitrarily well in operator norm by finite-rank kernels.Under any (hence all) of the above conditions, the Markov process is shown to have a purely discrete spectrum on a naturally associated weighted L∞space
Exponential ergodicity of the jump-diffusion CIR process
In this paper we study the jump-diffusion CIR process (shorted as JCIR),
which is an extension of the classical CIR model. The jumps of the JCIR are
introduced with the help of a pure-jump L\'evy process . Under
some suitable conditions on the L\'evy measure of , we derive a
lower bound for the transition densities of the JCIR process. We also find some
sufficient condition guaranteeing the existence of a Forster-Lyapunov function
for the JCIR process, which allows us to prove its exponential ergodicity.Comment: 14 page
A Probabilistic Look at Growth-Fragmentation Equations
International audienceIn this note, we consider general growth-fragmentation equations from a probabilistic point of view. Using Foster-Lyapunov techniques, we study the recurrence of the associated Markov process depending on the growth and fragmentation rates. We prove the existence and uniqueness of its stationary distribution, and we are able to derive precise bounds for its tails in the neighborhoods of both 0 and +∞. This study is systematically compared to the results obtained so far in the literature for this class of integro-differential equations
Invariant, super and quasi-martingale functions of a Markov process
We identify the linear space spanned by the real-valued excessive functions
of a Markov process with the set of those functions which are quasimartingales
when we compose them with the process. Applications to semi-Dirichlet forms are
given. We provide a unifying result which clarifies the relations between
harmonic, co-harmonic, invariant, co-invariant, martingale and co-martingale
functions, showing that in the conservative case they are all the same.
Finally, using the co-excessive functions, we present a two-step approach to
the existence of invariant probability measures
Relative Value Iteration for Stochastic Differential Games
We study zero-sum stochastic differential games with player dynamics governed
by a nondegenerate controlled diffusion process. Under the assumption of
uniform stability, we establish the existence of a solution to the Isaac's
equation for the ergodic game and characterize the optimal stationary
strategies. The data is not assumed to be bounded, nor do we assume geometric
ergodicity. Thus our results extend previous work in the literature. We also
study a relative value iteration scheme that takes the form of a parabolic
Isaac's equation. Under the hypothesis of geometric ergodicity we show that the
relative value iteration converges to the elliptic Isaac's equation as time
goes to infinity. We use these results to establish convergence of the relative
value iteration for risk-sensitive control problems under an asymptotic
flatness assumption
Multiplicative random walk Metropolis-Hastings on the real line
In this article we propose multiplication based random walk Metropolis
Hastings (MH) algorithm on the real line. We call it the random dive MH (RDMH)
algorithm. This algorithm, even if simple to apply, was not studied earlier in
Markov chain Monte Carlo literature. The associated kernel is shown to have
standard properties like irreducibility, aperiodicity and Harris recurrence
under some mild assumptions. These ensure basic convergence (ergodicity) of the
kernel. Further the kernel is shown to be geometric ergodic for a large class
of target densities on . This class even contains realistic target
densities for which random walk or Langevin MH are not geometrically ergodic.
Three simulation studies are given to demonstrate the mixing property and
superiority of RDMH to standard MH algorithms on real line. A share-price
return data is also analyzed and the results are compared with those available
in the literature
Ergodic properties of quasi-Markovian generalized Langevin equations with configuration dependent noise and non-conservative force
We discuss the ergodic properties of quasi-Markovian stochastic differential
equations, providing general conditions that ensure existence and uniqueness of
a smooth invariant distribution and exponential convergence of the evolution
operator in suitably weighted spaces, which implies the validity
of central limit theorem for the respective solution processes. The main new
result is an ergodicity condition for the generalized Langevin equation with
configuration-dependent noise and (non-)conservative force
Fluid and Diffusion Limits for Bike Sharing Systems
Bike sharing systems have rapidly developed around the world, and they are
served as a promising strategy to improve urban traffic congestion and to
decrease polluting gas emissions. So far performance analysis of bike sharing
systems always exists many difficulties and challenges under some more general
factors. In this paper, a more general large-scale bike sharing system is
discussed by means of heavy traffic approximation of multiclass closed queueing
networks with non-exponential factors. Based on this, the fluid scaled
equations and the diffusion scaled equations are established by means of the
numbers of bikes both at the stations and on the roads, respectively.
Furthermore, the scaling processes for the numbers of bikes both at the
stations and on the roads are proved to converge in distribution to a
semimartingale reflecting Brownian motion (SRBM) in a -dimensional box,
and also the fluid and diffusion limit theorems are obtained. Furthermore,
performance analysis of the bike sharing system is provided. Thus the results
and methodology of this paper provide new highlight in the study of more
general large-scale bike sharing systems.Comment: 34 pages, 1 figure
A Brownian particle in a microscopic periodic potential
We study a model for a massive test particle in a microscopic periodic
potential and interacting with a reservoir of light particles. In the regime
considered, the fluctuations in the test particle's momentum resulting from
collisions typically outweigh the shifts in momentum generated by the periodic
force, and so the force is effectively a perturbative contribution. The
mathematical starting point is an idealized reduced dynamics for the test
particle given by a linear Boltzmann equation. In the limit that the mass ratio
of a single reservoir particle to the test particle tends to zero, we show that
there is convergence to the Ornstein-Uhlenbeck process under the standard
normalizations for the test particle variables. Our analysis is primarily
directed towards bounding the perturbative effect of the periodic potential on
the particle's momentum.Comment: 60 pages. We reorganized the article and made a few simplifications
of the conten
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