1,220 research outputs found
Markov Chain Monte Carlo Method without Detailed Balance
We present a specific algorithm that generally satisfies the balance
condition without imposing the detailed balance in the Markov chain Monte
Carlo. In our algorithm, the average rejection rate is minimized, and even
reduced to zero in many relevant cases. The absence of the detailed balance
also introduces a net stochastic flow in a configuration space, which further
boosts up the convergence. We demonstrate that the autocorrelation time of the
Potts model becomes more than 6 times shorter than that by the conventional
Metropolis algorithm. Based on the same concept, a bounce-free worm algorithm
for generic quantum spin models is formulated as well.Comment: 5 pages, 5 figure
Estimating population cardinal health state valuation models from individual ordinal (rank) health state preference data
Ranking exercises have routinely been used as warm-up exercises within health state valuation surveys. Very little use has been made of the information obtained in this process. Instead, research has focussed upon the analysis of health state valuation data obtained using the visual analogue scale, standard gamble and time trade off methods.
Thurstone’s law of comparative judgement postulates a stable relationship between ordinal and cardinal preferences, based upon the information provided by pairwise choices. McFadden proposed that this relationship could be modelled by estimating conditional logistic regression models where alternatives had been ranked. In this paper we report the estimation of such models for the Health Utilities Index Mark 2 and the SF-6D. The results are compared to the conventional regression models estimated from standard gamble data, and to the observed mean standard gamble health state valuations.
For both the HUI2 and the SF-6D, the models estimated using rank data are broadly comparable to the models estimated on standard gamble data and the predictive performance of these models is close to that of the standard gamble models. Our research indicates that rank data has the potential to provide useful insights into community health state preferences. However, important questions remain
Estimating population cardinal health state valuation models from individual ordinal (rank) health state preference data
Ranking exercises have routinely been used as warm-up exercises within health state valuation surveys. Very little use has been made of the information obtained in this process. Instead, research has focussed upon the analysis of health state valuation data obtained using the visual analogue scale, standard gamble and time trade off methods. Thurstone’s law of comparative judgement postulates a stable relationship between ordinal and cardinal preferences, based upon the information provided by pairwise choices. McFadden proposed that this relationship could be modelled by estimating conditional logistic regression models where alternatives had been ranked. In this paper we report the estimation of such models for the Health Utilities Index Mark 2 and the SF-6D. The results are compared to the conventional regression models estimated from standard gamble data, and to the observed mean standard gamble health state valuations. For both the HUI2 and the SF-6D, the models estimated using rank data are broadly comparable to the models estimated on standard gamble data and the predictive performance of these models is close to that of the standard gamble models. Our research indicates that rank data has the potential to provide useful insights into community health state preferences. However, important questions remain.health state valuation; HUI-2; SF-6D
Estimating population cardinal health state valuation models from individual ordinal (rank) health state preference data
Ranking exercises have routinely been used as warm-up exercises within health state valuation surveys. Very little use has been made of the information obtained in this process. Instead, research has focussed upon the analysis of health state valuation data obtained using the visual analogue scale, standard gamble and time trade off methods.
Thurstone’s law of comparative judgement postulates a stable relationship between ordinal and cardinal preferences, based upon the information provided by pairwise choices. McFadden proposed that this relationship could be modelled by estimating conditional logistic regression models where alternatives had been ranked. In this paper we report the estimation of such models for the Health Utilities Index Mark 2 and the SF-6D. The results are compared to the conventional regression models estimated from standard gamble data, and to the observed mean standard gamble health state valuations.
For both the HUI2 and the SF-6D, the models estimated using rank data are broadly comparable to the models estimated on standard gamble data and the predictive performance of these models is close to that of the standard gamble models. Our research indicates that rank data has the potential to provide useful insights into community health state preferences. However, important questions remain
Mountain trail formation and the active walker model
We extend the active walker model to address the formation of paths on
gradients, which have been observed to have a zigzag form. Our extension
includes a new rule which prohibits direct descent or ascent on steep inclines,
simulating aversion to falling. Further augmentation of the model stops walkers
from changing direction very rapidly as that would likely lead to a fall. The
extended model predicts paths with qualitatively similar forms to the observed
trails, but only if the terms suppressing sudden direction changes are
included. The need to include terms into the model that stop rapid direction
change when simulating mountain trails indicates that a similar rule should
also be included in the standard active walker model.Comment: Introduction improved. Analysis of discretization errors added.
Calculations from alternative scheme include
A Bayesian approach to the follow-up of candidate gravitational wave signals
Ground-based gravitational wave laser interferometers (LIGO, GEO-600, Virgo
and Tama-300) have now reached high sensitivity and duty cycle. We present a
Bayesian evidence-based approach to the search for gravitational waves, in
particular aimed at the followup of candidate events generated by the analysis
pipeline. We introduce and demonstrate an efficient method to compute the
evidence and odds ratio between different models, and illustrate this approach
using the specific case of the gravitational wave signal generated during the
inspiral phase of binary systems, modelled at the leading quadrupole Newtonian
order, in synthetic noise. We show that the method is effective in detecting
signals at the detection threshold and it is robust against (some types of)
instrumental artefacts. The computational efficiency of this method makes it
scalable to the analysis of all the triggers generated by the analysis
pipelines to search for coalescing binaries in surveys with ground-based
interferometers, and to a whole variety of signal waveforms, characterised by a
larger number of parameters.Comment: 9 page
Using Markov chain Monte Carlo methods for estimating parameters with gravitational radiation data
We present a Bayesian approach to the problem of determining parameters for
coalescing binary systems observed with laser interferometric detectors. By
applying a Markov Chain Monte Carlo (MCMC) algorithm, specifically the Gibbs
sampler, we demonstrate the potential that MCMC techniques may hold for the
computation of posterior distributions of parameters of the binary system that
created the gravity radiation signal. We describe the use of the Gibbs sampler
method, and present examples whereby signals are detected and analyzed from
within noisy data.Comment: 21 pages, 10 figure
Bayesian coherent analysis of in-spiral gravitational wave signals with a detector network
The present operation of the ground-based network of gravitational-wave laser
interferometers in "enhanced" configuration brings the search for gravitational
waves into a regime where detection is highly plausible. The development of
techniques that allow us to discriminate a signal of astrophysical origin from
instrumental artefacts in the interferometer data and to extract the full range
of information are some of the primary goals of the current work. Here we
report the details of a Bayesian approach to the problem of inference for
gravitational wave observations using a network of instruments, for the
computation of the Bayes factor between two hypotheses and the evaluation of
the marginalised posterior density functions of the unknown model parameters.
The numerical algorithm to tackle the notoriously difficult problem of the
evaluation of large multi-dimensional integrals is based on a technique known
as Nested Sampling, which provides an attractive alternative to more
traditional Markov-chain Monte Carlo (MCMC) methods. We discuss the details of
the implementation of this algorithm and its performance against a Gaussian
model of the background noise, considering the specific case of the signal
produced by the in-spiral of binary systems of black holes and/or neutron
stars, although the method is completely general and can be applied to other
classes of sources. We also demonstrate the utility of this approach by
introducing a new coherence test to distinguish between the presence of a
coherent signal of astrophysical origin in the data of multiple instruments and
the presence of incoherent accidental artefacts, and the effects on the
estimation of the source parameters as a function of the number of instruments
in the network.Comment: 22 page
A Bayesian approach to discrete object detection in astronomical datasets
A Bayesian approach is presented for detecting and characterising the signal
from discrete objects embedded in a diffuse background. The approach centres
around the evaluation of the posterior distribution for the parameters of the
discrete objects, given the observed data, and defines the
theoretically-optimal procedure for parametrised object detection. Two
alternative strategies are investigated: the simultaneous detection of all the
discrete objects in the dataset, and the iterative detection of objects. In
both cases, the parameter space characterising the object(s) is explored using
Markov-Chain Monte-Carlo sampling. For the iterative detection of objects,
another approach is to locate the global maximum of the posterior at each
iteration using a simulated annealing downhill simplex algorithm. The
techniques are applied to a two-dimensional toy problem consisting of Gaussian
objects embedded in uncorrelated pixel noise. A cosmological illustration of
the iterative approach is also presented, in which the thermal and kinetic
Sunyaev-Zel'dovich effects from clusters of galaxies are detected in microwave
maps dominated by emission from primordial cosmic microwave background
anisotropies.Comment: 20 pages, 12 figures, accepted by MNRAS; contains some additional
material in response to referee's comment
Relic gravitational waves in the light of 7-year Wilkinson Microwave Anisotropy Probe data and improved prospects for the Planck mission
The new release of data from Wilkinson Microwave Anisotropy Probe improves
the observational status of relic gravitational waves. The 7-year results
enhance the indications of relic gravitational waves in the existing data and
change to the better the prospects of confident detection of relic
gravitational waves by the currently operating Planck satellite. We apply to
WMAP7 data the same methods of analysis that we used earlier [W. Zhao, D.
Baskaran, and L.P. Grishchuk, Phys. Rev. D 80, 083005 (2009)] with WMAP5 data.
We also revised by the same methods our previous analysis of WMAP3 data. It
follows from the examination of consecutive WMAP data releases that the maximum
likelihood value of the quadrupole ratio , which characterizes the amount of
relic gravitational waves, increases up to , and the interval
separating this value from the point (the hypothesis of no gravitational
waves) increases up to a level. The primordial spectra of density
perturbations and gravitational waves remain blue in the relevant interval of
wavelengths, but the spectral indices increase up to and
. Assuming that the maximum likelihood estimates of the perturbation
parameters that we found from WMAP7 data are the true values of the parameters,
we find that the signal-to-noise ratio for the detection of relic
gravitational waves by the Planck experiment increases up to , even
under pessimistic assumptions with regard to residual foreground contamination
and instrumental noises. We comment on theoretical frameworks that, in the case
of success, will be accepted or decisively rejected by the Planck observations.Comment: 27 pages, 12 (colour) figures. Published in Phys. Rev. D. V.3:
modifications made to reflect the published versio
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