726 research outputs found
The Computational Complexity of Estimating Convergence Time
An important problem in the implementation of Markov Chain Monte Carlo
algorithms is to determine the convergence time, or the number of iterations
before the chain is close to stationarity. For many Markov chains used in
practice this time is not known. Even in cases where the convergence time is
known to be polynomial, the theoretical bounds are often too crude to be
practical. Thus, practitioners like to carry out some form of statistical
analysis in order to assess convergence. This has led to the development of a
number of methods known as convergence diagnostics which attempt to diagnose
whether the Markov chain is far from stationarity. We study the problem of
testing convergence in the following settings and prove that the problem is
hard in a computational sense: Given a Markov chain that mixes rapidly, it is
hard for Statistical Zero Knowledge (SZK-hard) to distinguish whether starting
from a given state, the chain is close to stationarity by time t or far from
stationarity at time ct for a constant c. We show the problem is in AM
intersect coAM. Second, given a Markov chain that mixes rapidly it is coNP-hard
to distinguish whether it is close to stationarity by time t or far from
stationarity at time ct for a constant c. The problem is in coAM. Finally, it
is PSPACE-complete to distinguish whether the Markov chain is close to
stationarity by time t or far from being mixed at time ct for c at least 1
Modeling and characterization of TES-based detectors for the Ricochet experiment
Coherent elastic neutrino-nucleus scattering (CENS) offers a valuable
approach in searching for physics beyond the Standard Model. The Ricochet
experiment aims to perform a precision measurement of the CENS spectrum at
the Institut Laue-Langevin nuclear reactor with cryogenic solid-state
detectors. The experiment plans to employ an array of cryogenic thermal
detectors, each with a mass around 30 g and an energy threshold of sub-100 eV.
The array includes nine detectors read out by Transition-Edge Sensors (TES).
These TES based detectors will also serve as demonstrators for future neutrino
experiments with thousands of detectors. In this article we present an update
in the characterization and modeling of a prototype TES detector.Comment: Submitted to LTD20 proceedin
Faster Random Walks By Rewiring Online Social Networks On-The-Fly
Many online social networks feature restrictive web interfaces which only
allow the query of a user's local neighborhood through the interface. To enable
analytics over such an online social network through its restrictive web
interface, many recent efforts reuse the existing Markov Chain Monte Carlo
methods such as random walks to sample the social network and support analytics
based on the samples. The problem with such an approach, however, is the large
amount of queries often required (i.e., a long "mixing time") for a random walk
to reach a desired (stationary) sampling distribution.
In this paper, we consider a novel problem of enabling a faster random walk
over online social networks by "rewiring" the social network on-the-fly.
Specifically, we develop Modified TOpology (MTO)-Sampler which, by using only
information exposed by the restrictive web interface, constructs a "virtual"
overlay topology of the social network while performing a random walk, and
ensures that the random walk follows the modified overlay topology rather than
the original one. We show that MTO-Sampler not only provably enhances the
efficiency of sampling, but also achieves significant savings on query cost
over real-world online social networks such as Google Plus, Epinion etc.Comment: 15 pages, 14 figure, technical report for ICDE2013 paper. Appendix
has all the theorems' proofs; ICDE'201
Detection of contact failures with the Markov chain Monte Carlo method by using integral transformed measurements
This work deals with the solution of an inverse heat conduction problem aiming at the detection of contact failures in layered composites through the estimation of the contact conductance between the layers. The spatially varying contact conductance is estimated using a Bayesian formulation of the problem and a Markov chain Monte Carlo method, with infrared camera measurements of the transient temperature field on the surface of the body. The inverse analysis is formulated using a data compression scheme, where the temperature measurements are integral transformed with respect to the spatial variable. The present approach is evaluated using synthetic measurements and experimental data from controlled laboratory experiments. It is shown that only few transformed modes of the data are required for solving the inverse problem, thus providing substantial reduction of the computational time in the Markov chain Monte Carlo method, as well as regularization of the ill-posed problem.Indisponível
On the Geometric Ergodicity of Metropolis-Hastings Algorithms for Lattice Gaussian Sampling
Sampling from the lattice Gaussian distribution is emerging as an important
problem in coding and cryptography. In this paper, the classic
Metropolis-Hastings (MH) algorithm from Markov chain Monte Carlo (MCMC) methods
is adapted for lattice Gaussian sampling. Two MH-based algorithms are proposed,
which overcome the restriction suffered by the default Klein's algorithm. The
first one, referred to as the independent Metropolis-Hastings-Klein (MHK)
algorithm, tries to establish a Markov chain through an independent proposal
distribution. We show that the Markov chain arising from the independent MHK
algorithm is uniformly ergodic, namely, it converges to the stationary
distribution exponentially fast regardless of the initial state. Moreover, the
rate of convergence is explicitly calculated in terms of the theta series,
leading to a predictable mixing time. In order to further exploit the
convergence potential, a symmetric Metropolis-Klein (SMK) algorithm is
proposed. It is proven that the Markov chain induced by the SMK algorithm is
geometrically ergodic, where a reasonable selection of the initial state is
capable to enhance the convergence performance.Comment: Submitted to IEEE Transactions on Information Theor
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