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Per Sidén, Anders Eklund, David Bolin, Mattias Villani
Spatial whole-brain Bayesian modeling of task-related functional magnetic resonance imaging (fMRI) is a great computational challenge. Most of the currently proposed methods therefore do inference in subregions of the brain separately or do approximate inference without comparison to the true posterior distribution. A popular such method, which is now the standard method for Bayesian single subject analysis in the SPM software, is introduced in Penny et al. (2005b). The method processes the data slice-by-slice and uses an approximate variational Bayes (VB) estimation algorithm that enforces posterior independence between activity coefficients in different voxels...
November 19, 2016: NeuroImage
Matthew R Borths, Patricia A Holroyd, Erik R Seiffert
Hyaenodonta is a diverse, extinct group of carnivorous mammals that included weasel- to rhinoceros-sized species. The oldest-known hyaenodont fossils are from the middle Paleocene of North Africa and the antiquity of the group in Afro-Arabia led to the hypothesis that it originated there and dispersed to Asia, Europe, and North America. Here we describe two new hyaenodont species based on the oldest hyaenodont cranial specimens known from Afro-Arabia. The material was collected from the latest Eocene Locality 41 (L-41, ∼34 Ma) in the Fayum Depression, Egypt...
2016: PeerJ
Kai Wang, John N Ivan, Nalini Ravishanker, Eric Jackson
In an effort to improve traffic safety, there has been considerable interest in estimating crash prediction models and identifying factors contributing to crashes. To account for crash frequency variations among crash types and severities, crash prediction models have been estimated by type and severity. The univariate crash count models have been used by researchers to estimate crashes by crash type or severity, in which the crash counts by type or severity are assumed to be independent of one another and modelled separately...
November 12, 2016: Accident; Analysis and Prevention
Abhishek Chaudhary, Mohamed M Hantush
Model uncertainty estimation and risk assessment is essential to environmental management and informed decision making on pollution mitigation strategies. In this study, we apply a probabilistic methodology, which combines Bayesian Monte Carlo simulation and Maximum Likelihood estimation (BMCML) to calibrate a lake oxygen recovery model. We first derive an analytical solution of the differential equation governing lake-averaged oxygen dynamics as a function of time-variable wind speed. Statistical inferences on model parameters and predictive uncertainty are then drawn by Bayesian conditioning of the analytical solution on observed daily wind speed and oxygen concentration data obtained from an earlier study during two recovery periods on a eutrophic lake in upper state New York...
November 3, 2016: Water Research
Jianghai Zhao, Hui Zhang, Lunshou Wei, Shuping Xie, Zhimin Suo
A small proportion of hepatocellular carcinoma (HCC) patients are suitable for surgical resections and various minimally invasive procedures have been introduced as alternatives to surgical resections. However, the relative efficacy of minimally invasive procedures remains to be studied in the current literature. Several popular minimally invasive procedures (monotherapy or combined therapies) were selected for comparison and their relative long-term efficacy were determined by using the statistics of hazard ratio (HR) which evaluates the survival status of HCC patients in one, two, three and four years, respectively...
November 7, 2016: Oncotarget
Loic Le Folgoc, Herve Delingette, Antonio Criminisi, Nicholas Ayache
We investigate uncertainty quantification under a sparse Bayesian model of medical image registration. Bayesian modelling has proven powerful to automate the tuning of registration hyperparameters, such as the trade-off between the data and regularization functionals. Sparsity-inducing priors have recently been used to render the parametrization itself adaptive and data-driven. The sparse prior on transformation parameters effectively favors the use of coarse basis functions to capture the global trends in the visible motion while finer, highly localized bases are introduced only in the presence of coherent image information and motion...
November 1, 2016: IEEE Transactions on Medical Imaging
Diepreye Ayabina, Charlotte Hendon-Dunn, Joanna Bacon, Caroline Colijn
Drug resistance to tuberculosis (TB) has become more widespread over the past decade. As such, understanding the emergence and fitness of antibiotic-resistant subpopulations is crucial for the development of new interventions. Here we use a simple mathematical model to explain the differences in the response to isoniazid (INH) of Mycobacterium tuberculosis cells cultured under two growth rates in a chemostat. We obtain posterior distributions of model parameters consistent with data using a Markov chain Monte Carlo (MCMC) method...
November 2016: Journal of the Royal Society, Interface
Chun Wang, Gongjun Xu, Zhuoran Shang
Statistical methods for identifying aberrances on psychological and educational tests are pivotal to detect flaws in the design of a test or irregular behavior of test takers. Two approaches have been taken in the past to address the challenge of aberrant behavior detection, which are (1) modeling aberrant behavior via mixture modeling methods, and (2) flagging aberrant behavior via residual based outlier detection methods. In this paper, we propose a two-stage method that is conceived of as a combination of both approaches...
October 28, 2016: Psychometrika
Ali A Al-Shomrani, A I Shawky, Osama H Arif, Muhammad Aslam
This paper focuses on the application of Markov Chain Monte Carlo (MCMC) technique for estimating the parameters of log-logistic (LL) distribution which is dependent on a complete sample. To find Bayesian estimates for the parameters of the LL model OpenBUGS-established software for Bayesian analysis based on MCMC technique, is employed. It is presumed that samples for independent non informative set of priors for estimating LL parameters are drawn from posterior density function. A proposed module was developed and incorporated in OpenBUGS to estimate the Bayes estimators of the LL distribution...
2016: SpringerPlus
Kokouvi Gamado, George Streftaris, Stan Zachary
Under-reporting in epidemics, when it is ignored, leads to under-estimation of the infection rate and therefore of the reproduction number. In the case of stochastic models with temporal data, a usual approach for dealing with such issues is to apply data augmentation techniques through Bayesian methodology. Departing from earlier literature approaches implemented using reversible jump Markov chain Monte Carlo (RJMCMC) techniques, we make use of approximations to obtain faster estimation with simple MCMC. Comparisons among the methods developed here, and with the RJMCMC approach, are carried out and highlight that approximation-based methodology offers useful alternative inference tools for large epidemics, with a good trade-off between time cost and accuracy...
October 26, 2016: Journal of Mathematical Biology
F Spencer Koerner, John R Anderson, Jon M Fincham, Robert E Kass
Many functional neuroimaging-based studies involve repetitions of a task that may require several phases, or states, of mental activity. An appealing idea is to use relevant brain regions to identify the states. We developed a novel change-point methodology that adapts to the repeated trial structure of such experiments by assuming the number of states stays fixed across similar trials while allowing the timing of change-points to change across trials. Model fitting is based on reversible-jump MCMC. Simulation studies verified its ability to identify change-points successfully...
October 26, 2016: Statistics in Medicine
Noor-Ul-Huda Ghori, Atif Shafique, Muhammad Qasim Hayat, Sadia Anjum
Hepatitis C Virus (HCV) is the most prevalent human pathogen in Pakistan and is the major cause of liver cirrhosis and hepatocellular carcinoma in infected patients. It has shifted from being hypo-endemic to being hyper-endemic. There was no information about the origin and evolution of the local variants. Here we use newly developed phyloinformatic methods of sequence analysis to conduct the first comprehensive investigation of the evolutionary and biogeographic history in unprecedented detail and breadth...
2016: PloS One
Lizhen Xu, Radu V Craiu, Lei Sun, Andrew D Paterson
Motivated by genetic association studies of pleiotropy, we propose a Bayesian latent variable approach to jointly study multiple outcomes. The models studied here can incorporate both continuous and binary responses, and can account for serial and cluster correlations. We consider Bayesian estimation for the model parameters, and we develop a novel MCMC algorithm that builds upon hierarchical centering and parameter expansion techniques to efficiently sample from the posterior distribution. We evaluate the proposed method via extensive simulations and demonstrate its utility with an application to aa association study of various complication outcomes related to type 1 diabetes...
2016: Journal of Computational and Graphical Statistics
Keisuke Yoshihara, Minh Nhat Le, Koo Nagasawa, Hiroyuki Tsukagoshi, Hien Anh Nguyen, Michiko Toizumi, Hiroyuki Moriuchi, Masahiro Hashizume, Koya Ariyoshi, Duc Anh Dang, Hirokazu Kimura, Lay-Myint Yoshida
We performed molecular evolutionary analyses of the G gene C-terminal 3rd hypervariable region of RSV-A genotypes NA1 and ON1 strains from the paediatric acute respiratory infection patients in central Vietnam during the 2010-2012 study period. Time-scaled phylogenetic analyses were performed using Bayesian Markov Chain Monte Carlo (MCMC) method, and pairwise distances (p-distances) were calculated. Bayesian Skyline Plot (BSP) was constructed to analyze the time-trend relative genetic diversity of central Vietnam RSV-A strains...
November 2016: Infection, Genetics and Evolution
Prathiba Natesan, Ratna Nandakumar, Tom Minka, Jonathan D Rubright
This study investigated the impact of three prior distributions: matched, standard vague, and hierarchical in Bayesian estimation parameter recovery in two and one parameter models. Two Bayesian estimation methods were utilized: Markov chain Monte Carlo (MCMC) and the relatively new, Variational Bayesian (VB). Conditional (CML) and Marginal Maximum Likelihood (MML) estimates were used as baseline methods for comparison. Vague priors produced large errors or convergence issues and are not recommended. For both MCMC and VB, the hierarchical and matched priors showed the lowest root mean squared errors (RMSEs) for ability estimates; RMSEs of difficulty estimates were similar across estimation methods...
2016: Frontiers in Psychology
Valentina Clamer, Ilaria Dorigatti, Laura Fumanelli, Caterina Rizzo, Andrea Pugliese
BACKGROUND: Epidemic models are being extensively used to understand the main pathways of spread of infectious diseases, and thus to assess control methods. Schools are well known to represent hot spots for epidemic spread; hence, understanding typical patterns of infection transmission within schools is crucial for designing adequate control strategies. The attention that was given to the 2009 A/H1N1pdm09 flu pandemic has made it possible to collect detailed data on the occurrence of influenza-like illness (ILI) symptoms in two primary schools of Trento, Italy...
October 12, 2016: Theoretical Biology & Medical Modelling
Tayeb Mohammadi, Soleiman Kheiri, Morteza Sedehi
Recognizing the factors affecting the number of blood donation and blood deferral has a major impact on blood transfusion. There is a positive correlation between the variables "number of blood donation" and "number of blood deferral": as the number of return for donation increases, so does the number of blood deferral. On the other hand, due to the fact that many donors never return to donate, there is an extra zero frequency for both of the above-mentioned variables. In this study, in order to apply the correlation and to explain the frequency of the excessive zero, the bivariate zero-inflated Poisson regression model was used for joint modeling of the number of blood donation and number of blood deferral...
2016: Computational and Mathematical Methods in Medicine
Jiangan Xie, Lili Zhao, Shangbo Zhou, Yongqun He
Vaccinations often induce various adverse events (AEs), and sometimes serious AEs (SAEs). While many vaccines are used in combination, the effects of vaccine-vaccine interactions (VVIs) on vaccine AEs are rarely studied. In this study, AE profiles induced by hepatitis A vaccine (Havrix), hepatitis B vaccine (Engerix-B), and hepatitis A and B combination vaccine (Twinrix) were studied using the VAERS data. From May 2001 to January 2015, VAERS recorded 941, 3,885, and 1,624 AE case reports where patients aged at least 18 years old were vaccinated with only Havrix, Engerix-B, and Twinrix, respectively...
October 3, 2016: Scientific Reports
Pranjal Vachaspati, Tandy Warnow
MOTIVATION: The estimation of phylogenetic trees is a major part of many biological dataset analyses, but maximum likelihood approaches are NP-hard and Bayesian MCMC methods do not scale well to even moderate-sized datasets. Supertree methods, which are used to construct trees from trees computed on subsets, are critically important tools for enabling the statistical estimation of phylogenies for large and potentially heterogeneous datasets . Supertree estimation is itself NP-hard, and no current supertree method has sufficient accuracy and scalability to provide good accuracy on the large datasets that supertree methods were designed for, containing thousands of species and many subset trees...
September 23, 2016: Bioinformatics
Remco Bouckaert
BACKGROUND: Techniques for reconstructing geographical history along a phylogeny can answer many questions of interest about the geographical origins of species. Bayesian models based on the assumption that taxa move through a diffusion process have found many applications. However, these methods rely on diffusion processes on a plane, and do not take the spherical nature of our planet in account. Performing an analysis that covers the whole world thus does not take in account the distortions caused by projections like the Mercator projection...
2016: PeerJ
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