331 research outputs found
Influence on disease spread dynamics of herd characteristics in a structured livestock industry
Studies of between-herd contacts may provide important insight to disease transmission dynamics. By comparing the result from models with different levels of detail in the description of animal movement, we studied how factors influence the final epidemic size as well as the dynamic behaviour of an outbreak. We investigated the effect of contact heterogeneity of pig herds in Sweden due to herd size, between-herd distance and production type. Our comparative study suggests that the production-type structure is the most influential factor. Hence, our results imply that production type is the most important factor to obtain valid data for and include when modelling and analysing this system. The study also revealed that all included factors reduce the final epidemic size and also have yet more diverse effects on initial rate of disease spread. This implies that a large set of factors ought to be included to assess relevant predictions when modelling disease spread between herds. Furthermore, our results show that a more detailed model changes predictions regarding the variability in the outbreak dynamics and conclude that this is an important factor to consider in risk assessment
Tests of Bayesian Model Selection Techniques for Gravitational Wave Astronomy
The analysis of gravitational wave data involves many model selection
problems. The most important example is the detection problem of selecting
between the data being consistent with instrument noise alone, or instrument
noise and a gravitational wave signal. The analysis of data from ground based
gravitational wave detectors is mostly conducted using classical statistics,
and methods such as the Neyman-Pearson criteria are used for model selection.
Future space based detectors, such as the \emph{Laser Interferometer Space
Antenna} (LISA), are expected to produced rich data streams containing the
signals from many millions of sources. Determining the number of sources that
are resolvable, and the most appropriate description of each source poses a
challenging model selection problem that may best be addressed in a Bayesian
framework. An important class of LISA sources are the millions of low-mass
binary systems within our own galaxy, tens of thousands of which will be
detectable. Not only are the number of sources unknown, but so are the number
of parameters required to model the waveforms. For example, a significant
subset of the resolvable galactic binaries will exhibit orbital frequency
evolution, while a smaller number will have measurable eccentricity. In the
Bayesian approach to model selection one needs to compute the Bayes factor
between competing models. Here we explore various methods for computing Bayes
factors in the context of determining which galactic binaries have measurable
frequency evolution. The methods explored include a Reverse Jump Markov Chain
Monte Carlo (RJMCMC) algorithm, Savage-Dickie density ratios, the Schwarz-Bayes
Information Criterion (BIC), and the Laplace approximation to the model
evidence. We find good agreement between all of the approaches.Comment: 11 pages, 6 figure
A Solution to the Galactic Foreground Problem for LISA
Low frequency gravitational wave detectors, such as the Laser Interferometer
Space Antenna (LISA), will have to contend with large foregrounds produced by
millions of compact galactic binaries in our galaxy. While these galactic
signals are interesting in their own right, the unresolved component can
obscure other sources. The science yield for the LISA mission can be improved
if the brighter and more isolated foreground sources can be identified and
regressed from the data. Since the signals overlap with one another we are
faced with a ``cocktail party'' problem of picking out individual conversations
in a crowded room. Here we present and implement an end-to-end solution to the
galactic foreground problem that is able to resolve tens of thousands of
sources from across the LISA band. Our algorithm employs a variant of the
Markov Chain Monte Carlo (MCMC) method, which we call the Blocked Annealed
Metropolis-Hastings (BAM) algorithm. Following a description of the algorithm
and its implementation, we give several examples ranging from searches for a
single source to searches for hundreds of overlapping sources. Our examples
include data sets from the first round of Mock LISA Data Challenges.Comment: 19 pages, 27 figure
Nonparametric Reconstruction of the Dark Energy Equation of State from Diverse Data Sets
The cause of the accelerated expansion of the Universe poses one of the most
fundamental questions in physics today. In the absence of a compelling theory
to explain the observations, a first task is to develop a robust phenomenology.
If the acceleration is driven by some form of dark energy, then, the
phenomenology is determined by the dark energy equation of state w. A major aim
of ongoing and upcoming cosmological surveys is to measure w and its time
dependence at high accuracy. Since w(z) is not directly accessible to
measurement, powerful reconstruction methods are needed to extract it reliably
from observations. We have recently introduced a new reconstruction method for
w(z) based on Gaussian process modeling. This method can capture nontrivial
time-dependences in w(z) and, most importantly, it yields controlled and
unbaised error estimates. In this paper we extend the method to include a
diverse set of measurements: baryon acoustic oscillations, cosmic microwave
background measurements, and supernova data. We analyze currently available
data sets and present the resulting constraints on w(z), finding that current
observations are in very good agreement with a cosmological constant. In
addition we explore how well our method captures nontrivial behavior of w(z) by
analyzing simulated data assuming high-quality observations from future
surveys. We find that the baryon acoustic oscillation measurements by
themselves already lead to remarkably good reconstruction results and that the
combination of different high-quality probes allows us to reconstruct w(z) very
reliably with small error bounds.Comment: 14 pages, 9 figures, 3 table
Nonparametric Reconstruction of the Dark Energy Equation of State
A basic aim of ongoing and upcoming cosmological surveys is to unravel the
mystery of dark energy. In the absence of a compelling theory to test, a
natural approach is to better characterize the properties of dark energy in
search of clues that can lead to a more fundamental understanding. One way to
view this characterization is the improved determination of the
redshift-dependence of the dark energy equation of state parameter, w(z). To do
this requires a robust and bias-free method for reconstructing w(z) from data
that does not rely on restrictive expansion schemes or assumed functional forms
for w(z). We present a new nonparametric reconstruction method that solves for
w(z) as a statistical inverse problem, based on a Gaussian Process
representation. This method reliably captures nontrivial behavior of w(z) and
provides controlled error bounds. We demonstrate the power of the method on
different sets of simulated supernova data; the approach can be easily extended
to include diverse cosmological probes.Comment: 16 pages, 11 figures, accepted for publication in Physical Review
A Bayesian Approach to the Detection Problem in Gravitational Wave Astronomy
The analysis of data from gravitational wave detectors can be divided into
three phases: search, characterization, and evaluation. The evaluation of the
detection - determining whether a candidate event is astrophysical in origin or
some artifact created by instrument noise - is a crucial step in the analysis.
The on-going analyses of data from ground based detectors employ a frequentist
approach to the detection problem. A detection statistic is chosen, for which
background levels and detection efficiencies are estimated from Monte Carlo
studies. This approach frames the detection problem in terms of an infinite
collection of trials, with the actual measurement corresponding to some
realization of this hypothetical set. Here we explore an alternative, Bayesian
approach to the detection problem, that considers prior information and the
actual data in hand. Our particular focus is on the computational techniques
used to implement the Bayesian analysis. We find that the Parallel Tempered
Markov Chain Monte Carlo (PTMCMC) algorithm is able to address all three phases
of the anaylsis in a coherent framework. The signals are found by locating the
posterior modes, the model parameters are characterized by mapping out the
joint posterior distribution, and finally, the model evidence is computed by
thermodynamic integration. As a demonstration, we consider the detection
problem of selecting between models describing the data as instrument noise, or
instrument noise plus the signal from a single compact galactic binary. The
evidence ratios, or Bayes factors, computed by the PTMCMC algorithm are found
to be in close agreement with those computed using a Reversible Jump Markov
Chain Monte Carlo algorithm.Comment: 19 pages, 12 figures, revised to address referee's comment
Count time series prediction using particle filters
Non-Gaussian dynamic models are proposed to analyse time series of counts. Three models are proposed for responses generated by a Poisson, a negative binomial and a mixture of Poisson distributions. The parameters of these distributions are allowed to vary dynamically according to state space models. Particle filters or sequential Monte Carlo methods are used for inference and forecasting purposes. The performance of the proposed methodology is evaluated by two simulation studies for the Poisson and the negative binomial models. The methodology is illustrated by considering data consisting of medical contacts of schoolchildren suffering from asthma in England
LISA Data Analysis using MCMC methods
The Laser Interferometer Space Antenna (LISA) is expected to simultaneously
detect many thousands of low frequency gravitational wave signals. This
presents a data analysis challenge that is very different to the one
encountered in ground based gravitational wave astronomy. LISA data analysis
requires the identification of individual signals from a data stream containing
an unknown number of overlapping signals. Because of the signal overlaps, a
global fit to all the signals has to be performed in order to avoid biasing the
solution. However, performing such a global fit requires the exploration of an
enormous parameter space with a dimension upwards of 50,000. Markov Chain Monte
Carlo (MCMC) methods offer a very promising solution to the LISA data analysis
problem. MCMC algorithms are able to efficiently explore large parameter
spaces, simultaneously providing parameter estimates, error analyses and even
model selection. Here we present the first application of MCMC methods to
simulated LISA data and demonstrate the great potential of the MCMC approach.
Our implementation uses a generalized F-statistic to evaluate the likelihoods,
and simulated annealing to speed convergence of the Markov chains. As a final
step we super-cool the chains to extract maximum likelihood estimates, and
estimates of the Bayes factors for competing models. We find that the MCMC
approach is able to correctly identify the number of signals present, extract
the source parameters, and return error estimates consistent with Fisher
information matrix predictions.Comment: 14 pages, 7 figure
Company family, innovation and colombian graphic industry: a bayesian estimation of a logistical model
This study presents a comparative analysis of the management of innovation among family and non-family companies of the Graphic Communication Industry in Colombia. For which a questionnaire was applied in order to know the divergences in the innovation process carried out by these two types of organizations. From this, the methodology of Generalized Linear Models (MLG) was used and the Bayesian inference was used on the parameters of the model, analyzing the effect of the family business, the products that commercialize on the management of innovation in goods observed as a product tangible Obtaining in this way, the identification of some characteristics of innovation management and divergences with non-family companies, among them: a tendency towards the type of preferred innovation, the different sources and objectives to innovate, and the factors that hinder its process of innovation
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