1,908 research outputs found
Computational Bayesian Methods Applied to Complex Problems in Bio and Astro Statistics
In this dissertation we apply computational Bayesian methods to three distinct problems. In the first chapter, we address the issue of unrealistic covariance matrices used to estimate collision probabilities. We model covariance matrices with a Bayesian Normal-Inverse-Wishart model, which we fit with Gibbs sampling. In the second chapter, we are interested in determining the sample sizes necessary to achieve a particular interval width and establish non-inferiority in the analysis of prevalences using two fallible tests. To this end, we use a third order asymptotic approximation. In the third chapter, we wish to synthesize evidence across multiple domains in measurements taken longitudinally across time, featuring a substantial amount of structurally missing data, and fit the model with Hamiltonian Monte Carlo in a simulation to analyze how estimates of a parameter of interest change across sample sizes
Approximation schemes for the dynamics of diluted spin models: the Ising ferromagnet on a Bethe lattice
We discuss analytical approximation schemes for the dynamics of diluted spin
models. The original dynamics of the complete set of degrees of freedom is
replaced by a hierarchy of equations including an increasing number of global
observables, which can be closed approximately at different levels of the
hierarchy. We illustrate this method on the simple example of the Ising
ferromagnet on a Bethe lattice, investigating the first three possible
closures, which are all exact in the long time limit, and which yield more and
more accurate predictions for the finite-time behavior. We also investigate the
critical region around the phase transition, and the behavior of two-time
correlation functions. We finally underline the close relationship between this
approach and the dynamical replica theory under the assumption of replica
symmetry.Comment: 21 pages, 5 figure
Bayesian statistics and modelling
Bayesian statistics is an approach to data analysis based on Bayesâ theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. This Primer describes the stages involved in Bayesian analysis, from specifying the prior and data models to deriving inference, model checking and refinement. We discuss the importance of prior and posterior predictive checking, selecting a proper technique for sampling from a posterior distribution, variational inference and variable selection. Examples of successful applications of Bayesian analysis across various research fields are provided, including in social sciences, ecology, genetics, medicine and more. We propose strategies for reproducibility and reporting standards, outlining an updated WAMBS (when to Worry and how to Avoid the Misuse of Bayesian Statistics) checklist. Finally, we outline the impact of Bayesian analysis on artificial intelligence, a major goal in the next decade
Explosive Percolation: Unusual Transitions of a Simple Model
In this paper we review the recent advances on explosive percolation, a very
sharp phase transition first observed by Achlioptas et al. (Science, 2009).
There a simple model was proposed, which changed slightly the classical
percolation process so that the emergence of the spanning cluster is delayed.
This slight modification turns out to have a great impact on the percolation
phase transition. The resulting transition is so sharp that it was termed
explosive, and it was at first considered to be discontinuous. This surprising
fact stimulated considerable interest in "Achlioptas processes". Later work,
however, showed that the transition is continuous (at least for Achlioptas
processes on Erdos networks), but with very unusual finite size scaling. We
present a review of the field, indicate open "problems" and propose directions
for future research.Comment: 27 pages, 4 figures, Review pape
Simulations in statistical physics and biology: some applications
One of the most active areas of physics in the last decades has been that of
critical phenomena, and Monte Carlo simulations have played an important role
as a guide for the validation and prediction of system properties close to the
critical points. The kind of phase transitions occurring for the Betts lattice
(lattice constructed removing 1/7 of the sites from the triangular lattice)
have been studied before with the Potts model for the values q=3, ferromagnetic
and antiferromagnetic regime. Here, we add up to this research line the
ferromagnetic case for q=4 and 5. In the first case, the critical exponents are
estimated for the second order transition, whereas for the latter case the
histogram method is applied for the occurring first order transition.
Additionally, Domany's Monte Carlo based clustering technique mainly used to
group genes similar in their expression levels is reviewed. Finally, a control
theory tool --an adaptive observer-- is applied to estimate the exponent
parameter involved in the well-known Gompertz curve. By treating all these
subjects our aim is to stress the importance of cooperation between distinct
disciplines in addressing the complex problems arising in biology.
Contents: Chapter 1 - Monte Carlo simulations in stat. physics; Chapter 2: MC
simulations in biology; Chapter 3: Gompertz equationComment: 82 pages, 33 figures, 4 tables, somewhat reduced version of the M.Sc.
thesis defended in Jan. 2006 at IPICyT, San Luis Potosi, Mx. (Supervisers:
Drs. R. Lopez-Sandoval and H.C. Rosu). Last sections 3.3 and 3.4 can be found
at http://lanl.arxiv.org/abs/physics/041108
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