18,158 research outputs found
Active Classification for POMDPs: a Kalman-like State Estimator
The problem of state tracking with active observation control is considered
for a system modeled by a discrete-time, finite-state Markov chain observed
through conditionally Gaussian measurement vectors. The measurement model
statistics are shaped by the underlying state and an exogenous control input,
which influence the observations' quality. Exploiting an innovations approach,
an approximate minimum mean-squared error (MMSE) filter is derived to estimate
the Markov chain system state. To optimize the control strategy, the associated
mean-squared error is used as an optimization criterion in a partially
observable Markov decision process formulation. A stochastic dynamic
programming algorithm is proposed to solve for the optimal solution. To enhance
the quality of system state estimates, approximate MMSE smoothing estimators
are also derived. Finally, the performance of the proposed framework is
illustrated on the problem of physical activity detection in wireless body
sensing networks. The power of the proposed framework lies within its ability
to accommodate a broad spectrum of active classification applications including
sensor management for object classification and tracking, estimation of sparse
signals and radar scheduling.Comment: 38 pages, 6 figure
Efficient computation of updated lower expectations for imprecise continuous-time hidden Markov chains
We consider the problem of performing inference with imprecise
continuous-time hidden Markov chains, that is, imprecise continuous-time Markov
chains that are augmented with random output variables whose distribution
depends on the hidden state of the chain. The prefix `imprecise' refers to the
fact that we do not consider a classical continuous-time Markov chain, but
replace it with a robust extension that allows us to represent various types of
model uncertainty, using the theory of imprecise probabilities. The inference
problem amounts to computing lower expectations of functions on the state-space
of the chain, given observations of the output variables. We develop and
investigate this problem with very few assumptions on the output variables; in
particular, they can be chosen to be either discrete or continuous random
variables. Our main result is a polynomial runtime algorithm to compute the
lower expectation of functions on the state-space at any given time-point,
given a collection of observations of the output variables
A survey of generalized inverses and their use in stochastic modelling
In many stochastic models, in particular Markov chains in discrete or continuous time and Markov
renewal processes, a Markov chain is present either directly or indirectly through some form of
embedding. The analysis of many problems of interest associated with these models, eg. stationary
distributions, moments of first passage time distributions and moments of occupation time random
variables, often concerns the solution of a system of linear equations involving I ā P, where P is the
transition matrix of a finite, irreducible, discrete time Markov chain.
Generalized inverses play an important role in the solution of such singular sets of equations. In this
paper we survey the application of generalized inverses to the aforementioned problems. The
presentation will include results concerning the analysis of perturbed systems and the characterization of
types of generalized inverses associated with Markovian kernels
Quasi-stationary distributions
This paper contains a survey of results related to quasi-stationary distributions, which arise in the setting of stochastic dynamical systems that eventually evanesce, and which may be useful in describing the long-term behaviour of such systems before evanescence. We are concerned mainly with continuous-time Markov chains over a finite or countably infinite state space, since these processes most often arise in applications, but will make reference to results for other processes where appropriate. Next to giving an historical account of the subject, we review the most important results on the existence and identification of quasi-stationary distributions for general Markov chains, and give special attention to birth-death processes and related models. Results on the question of whether a quasi-stationary distribution, given its existence, is indeed a good descriptor of the long-term behaviour of a system before evanescence, are reviewed as well. The paper is concluded with a summary of recent developments in numerical and approximation methods
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
We propose Kernel Hamiltonian Monte Carlo (KMC), a gradient-free adaptive
MCMC algorithm based on Hamiltonian Monte Carlo (HMC). On target densities
where classical HMC is not an option due to intractable gradients, KMC
adaptively learns the target's gradient structure by fitting an exponential
family model in a Reproducing Kernel Hilbert Space. Computational costs are
reduced by two novel efficient approximations to this gradient. While being
asymptotically exact, KMC mimics HMC in terms of sampling efficiency, and
offers substantial mixing improvements over state-of-the-art gradient free
samplers. We support our claims with experimental studies on both toy and
real-world applications, including Approximate Bayesian Computation and
exact-approximate MCMC.Comment: 20 pages, 7 figure
Efficient CSL Model Checking Using Stratification
For continuous-time Markov chains, the model-checking problem with respect to
continuous-time stochastic logic (CSL) has been introduced and shown to be
decidable by Aziz, Sanwal, Singhal and Brayton in 1996. Their proof can be
turned into an approximation algorithm with worse than exponential complexity.
In 2000, Baier, Haverkort, Hermanns and Katoen presented an efficient
polynomial-time approximation algorithm for the sublogic in which only binary
until is allowed. In this paper, we propose such an efficient polynomial-time
approximation algorithm for full CSL. The key to our method is the notion of
stratified CTMCs with respect to the CSL property to be checked. On a
stratified CTMC, the probability to satisfy a CSL path formula can be
approximated by a transient analysis in polynomial time (using uniformization).
We present a measure-preserving, linear-time and -space transformation of any
CTMC into an equivalent, stratified one. This makes the present work the
centerpiece of a broadly applicable full CSL model checker. Recently, the
decision algorithm by Aziz et al. was shown to work only for stratified CTMCs.
As an additional contribution, our measure-preserving transformation can be
used to ensure the decidability for general CTMCs.Comment: 18 pages, preprint for LMCS. An extended abstract appeared in ICALP
201
On limiting distributions of quantum Markov chains
In a quantum Markov chain, the temporal succession of states is modeled by
the repeated action of a "bistochastic quantum operation" on the density matrix
of a quantum system. Based on this conceptual framework, we derive some new
results concerning the evolution of a quantum system, including its long-term
behavior. Among our findings is the fact that the Cesro limit of any
quantum Markov chain always exists and equals the orthogonal projection of the
initial state upon the eigenspace of the unit eigenvalue of the bistochastic
quantum operation. Moreover, if the unit eigenvalue is the only eigenvalue on
the unit circle, then the quantum Markov chain converges in the conventional
sense to the said orthogonal projection. As a corollary, we offer a new
derivation of the classic result describing limiting distributions of unitary
quantum walks on finite graphs \cite{AAKV01}
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