266,697 research outputs found
Quantum Semi-Markov Processes
We construct a large class of non-Markovian master equations that describe
the dynamics of open quantum systems featuring strong memory effects, which
relies on a quantum generalization of the concept of classical semi-Markov
processes. General conditions for the complete positivity of the corresponding
quantum dynamical maps are formulated. The resulting non-Markovian quantum
processes allow the treatment of a variety of physical systems, as is
illustrated by means of various examples and applications, including quantum
optical systems and models of quantum transport.Comment: 4 pages, revtex, no figures, to appear in Phys. Rev. Let
Markov two-components processes
We propose Markov two-components processes (M2CP) as a probabilistic model of
asynchronous systems based on the trace semantics for concurrency. Considering
an asynchronous system distributed over two sites, we introduce concepts and
tools to manipulate random trajectories in an asynchronous framework: stopping
times, an Asynchronous Strong Markov property, recurrent and transient states
and irreducible components of asynchronous probabilistic processes. The
asynchrony assumption implies that there is no global totally ordered clock
ruling the system. Instead, time appears as partially ordered and random. We
construct and characterize M2CP through a finite family of transition matrices.
M2CP have a local independence property that guarantees that local components
are independent in the probabilistic sense, conditionally to their
synchronization constraints. A synchronization product of two Markov chains is
introduced, as a natural example of M2CP.Comment: 34 page
Feature Markov Decision Processes
General purpose intelligent learning agents cycle through (complex,non-MDP)
sequences of observations, actions, and rewards. On the other hand,
reinforcement learning is well-developed for small finite state Markov Decision
Processes (MDPs). So far it is an art performed by human designers to extract
the right state representation out of the bare observations, i.e. to reduce the
agent setup to the MDP framework. Before we can think of mechanizing this
search for suitable MDPs, we need a formal objective criterion. The main
contribution of this article is to develop such a criterion. I also integrate
the various parts into one learning algorithm. Extensions to more realistic
dynamic Bayesian networks are developed in a companion article.Comment: 7 page
Metastability in Markov processes
We present a formalism to describe slowly decaying systems in the context of
finite Markov chains obeying detailed balance. We show that phase space can be
partitioned into approximately decoupled regions, in which one may introduce
restricted Markov chains which are close to the original process but do not
leave these regions. Within this context, we identify the conditions under
which the decaying system can be considered to be in a metastable state.
Furthermore, we show that such metastable states can be described in
thermodynamic terms and define their free energy. This is accomplished showing
that the probability distribution describing the metastable state is indeed
proportional to the equilibrium distribution, as is commonly assumed. We test
the formalism numerically in the case of the two-dimensional kinetic Ising
model, using the Wang--Landau algorithm to show this proportionality
explicitly, and confirm that the proportionality constant is as derived in the
theory. Finally, we extend the formalism to situations in which a system can
have several metastable states.Comment: 30 pages, 5 figures; version with one higher quality figure available
at http://www.fis.unam.mx/~dsanders
Towards a General Theory of Stochastic Hybrid Systems
In this paper we set up a mathematical structure,
called Markov string, to obtaining a very general class of models for stochastic hybrid systems. Markov Strings are, in fact, a class of Markov processes, obtained by a
mixing mechanism of stochastic processes, introduced
by Meyer. We prove that Markov strings are strong Markov processes with the cadlag property. We then show how a very general class of stochastic hybrid processes can be embedded
in the framework of Markov strings. This class, which
is referred to as the General Stochastic Hybrid Systems (GSHS), includes as special cases all the classes of stochastic hybrid processes, proposed in the literature
Multiple-Environment Markov Decision Processes
We introduce Multi-Environment Markov Decision Processes (MEMDPs) which are
MDPs with a set of probabilistic transition functions. The goal in a MEMDP is
to synthesize a single controller with guaranteed performances against all
environments even though the environment is unknown a priori. While MEMDPs can
be seen as a special class of partially observable MDPs, we show that several
verification problems that are undecidable for partially observable MDPs, are
decidable for MEMDPs and sometimes have even efficient solutions
Markov cubature rules for polynomial processes
We study discretizations of polynomial processes using finite state Markov
processes satisfying suitable moment matching conditions. The states of these
Markov processes together with their transition probabilities can be
interpreted as Markov cubature rules. The polynomial property allows us to
study such rules using algebraic techniques. Markov cubature rules aid the
tractability of path-dependent tasks such as American option pricing in models
where the underlying factors are polynomial processes.Comment: 29 pages, 6 Figures, 2 Tables; forthcoming in Stochastic Processes
and their Application
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