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At least 1,621 records · Page 90Linked to original sources
PubMed2002

Oh brother, where art thou? A Bayes factor test for recombination with uncertain heritage.

Current methods to identify recombination between subtypes of human immunodeficiency virus 1 (HIV-1) fall into a sequential testing trap, in which significance is assessed conditional on parental representative sequences and crossover points (COPs) that maximize the same test statistic. We overcame this shortfall by testing for recombination while inferring parental heritage and COPs using an extended Bayesian multiple change-point model. The model assumes that aligned molecular sequence data consist of an unknown number of contiguous segments that may support alternative topologies or varying evolutionary pressures. We allowed for heterogeneity in the substitution process and specifically tested for intersubtype recombination using Bayes factors. We also developed a new class of priors to assess significance across a wide range of support for recombination in the data. We applied our method to three putative gag gene recombinants. HIV-1 isolate RW024 decisively supported recombination with an inferred parental heritage of AD and a COP 95% Bayesian credible interval of (1,152, 1,178) using the HXB2 numbering scheme. HIV-1 isolate VI557 barely supported recombination. HIV-1 isolate RF decisively rejected recombination as expected, given that the sequence is commonly used as a reference sequence for subtype B. We employed scaled regeneration quantile plots to assess convergence and found this approach convenient to use even for our variable dimensional model parameter space.

Algorithms↗
PubMed2006

A statistical test of the hypothesis that polyclonal intestinal tumors arise by random collision of initiated clones.

The random collision hypothesis is a mathematical idealization of intestinal tumor formation that can account for the polyclonal origin of tumors without requiring a mechanistic description of clonal interaction. Using data from recent polyclonality studies in mice, we develop a statistical procedure to test the random collision hypothesis. Elements from stochastic geometry and approximations due to Armitage (1949, Biometrika 36, 257-266) support a statistical model of tumor count data. Bayesian analysis yields the posterior distribution of the number of heterotypic tumors, from which p-values are computed to test random collision.

Animals↗
PubMed2004

Recursive penalized least squares solution for dynamical inverse problems of EEG generation.

In the dynamical inverse problem of electroencephalogram (EEG) generation where a specific dynamics for the electrical current distribution is assumed, we can impose general spatiotemporal constraints onto the solution by casting the problem into a state space representation and assuming a specific class of parametric models for the dynamics. The Akaike Bayesian Information Criterion (ABIC), which is based on the Type II likelihood, was used to estimate the parameters and evaluate the model. In addition, dynamic low-resolution brain electromagnetic tomography (LORETA), a new approach for estimating the current distribution is introduced. A recursive penalized least squares (RPLS) step forms the main element of our implementation. To obtain improved inverse solutions, dynamic LORETA exploits both spatial and temporal information, whereas LORETA uses only spatial information. A considerable improvement in performance compared to LORETA was found when dynamic LORETA was applied to simulated EEG data, and the new method was applied also to clinical EEG data.

Brain↗
PubMed2006

Different classification techniques considering brain computer interface applications.

In this work the application of different machine learning techniques for classification of mental tasks from electroencephalograph (EEG) signals is investigated. The main application for this research is the improvement of brain computer interface (BCI) systems. For this purpose, Bayesian graphical network, neural network, Bayesian quadratic, Fisher linear and hidden Markov model classifiers are applied to two known EEG datasets in the BCI field. The Bayesian network classifier is used for the first time in this work for classification of EEG signals. The Bayesian network appeared to have a significant accuracy and more consistent classification compared to the other four methods. In addition to classical correct classification accuracy criteria, the mutual information is also used to compare the classification results with other BCI groups.

Algorithms↗
PubMed2001

Analysis of ambulatory blood pressure monitor data using a hierarchical model incorporating restricted cubic splines and heterogeneous within-subject variances.

Hypertensive disorders of pregnancy are associated with significant maternal and foetal morbidity. Measurement of blood pressure remains the standard way of identifying individuals at risk. There is growing interest in the use of ambulatory blood pressure monitors (ABPM), which can record an individual's blood pressure many times over a 24-hour period. From a clinical perspective interest lies in the shape of the blood pressure profile over a 24-hour period and any differences in the profile between groups. We propose a two-level hierarchical linear model incorporating all ABPM data into a single model. We contrast a classical approach with a Bayesian approach using the results of a study of 206 pregnant women who were asked to wear an ABPM for 24 hours after referral to an obstetric day unit with high blood pressure. As the main interest lies in the shape of the profile, we use restricted cubic splines to model the mean profiles. The use of restricted cubic splines provides a flexible way to model the mean profiles and to make comparisons between groups. From examining the data and the fit of the model it is apparent that there were heterogeneous within-subject variances in that some women tend to have more variable blood pressure than others. Within the Bayesian framework it is relatively easy to incorporate a random effect to model the between-subject variation in the within-subject variances. Although there is substantial heterogeneity in the within-subject variances, allowing for this in the model has surprisingly little impact on the estimates of the mean profiles or their confidence/credible intervals. We thus demonstrate a powerful method for analysis of ABPM data and also demonstrate how heterogeneous within-subject variances can be modelled from a Bayesian perspective.

Bayes Theorem↗
PubMed1998

Measuring departures from Hardy-Weinberg: a Markov chain Monte Carlo method for estimating the inbreeding coefficient.

Many well-established statistical methods in genetics were developed in a climate of severe constraints on computational power. Recent advances in simulation methodology now bring modern, flexible statistical methods within the reach of scientists having access to a desktop workstation. We illustrate the potential advantages now available by considering the problem of assessing departures from Hardy-Weinberg (HW) equilibrium. Several hypothesis tests of HW have been established, as well as a variety of point estimation methods for the parameter which measures departures from HW under the inbreeding model. We propose a computational, Bayesian method for assessing departures from HW, which has a number of important advantages over existing approaches. The method incorporates the effects-of uncertainty about the nuisance parameters--the allele frequencies--as well as the boundary constraints on f (which are functions of the nuisance parameters). Results are naturally presented visually, exploiting the graphics capabilities of modern computer environments to allow straightforward interpretation. Perhaps most importantly, the method is founded on a flexible, likelihood-based modelling framework, which can incorporate the inbreeding model if appropriate, but also allows the assumptions of the model to he investigated and, if necessary, relaxed. Under appropriate conditions, information can be shared across loci and, possibly, across populations, leading to more precise estimation. The advantages of the method are illustrated by application both to simulated data and to data analysed by alternative methods in the recent literature.

Algorithms↗
PubMed2005

A multiple-feature framework for modelling and predicting transcription factor binding sites.

MOTIVATION: The identification of transcription factor binding sites in promoter sequences is an important problem, since it reveals information about the transcriptional regulation of genes. For analysing transcriptional regulation, computational approaches for predicting putative binding sites are applied. Commonly used stochastic models for binding sites are position-specific score matrices, which show weak predictive power. RESULTS: We have developed a probabilistic modelling approach, which allows to consider diverse characteristic binding site properties to obtain more accurate representations of binding sites. These properties are modelled as random variables in Bayesian networks, which are capable of dealing with dependencies among binding site properties. Cross-validation on several datasets shows improvements in the false positive error rate and the significance (P-value) of true binding sites.

Algorithms↗
PubMed2006

A generative sketch model for human hair analysis and synthesis.

In this paper, we present a generative sketch model for human hair analysis and synthesis. We treat hair images as 2D piecewise smooth vector (flow) fields and, thus, our representation is view-based in contrast to the physically-based 3D hair models in graphics. The generative model has three levels. The bottom level is the high-frequency band of the hair image. The middle level is a piecewise smooth vector field for the hair orientation, gradient strength, and growth directions. The top level is an attribute sketch graph for representing the discontinuities in the vector field. A sketch graph typically has a number of sketch curves which are divided into 11 types of directed primitives. Each primitive is a small window (say 5 x 7 pixels) where the orientations and growth directions are defined in parametric forms, for example, hair boundaries, occluding lines between hair strands, dividing lines on top of the hair, etc. In addition to the three level representation, we model the shading effects, i.e., the low-frequency band of the hair image, by a linear superposition of some Gaussian image bases and we encode the hair color by a color map. The inference algorithm is divided into two stages: 1) We compute the undirected orientation field and sketch graph from an input image and 2) we compute the hair growth direction forthe sketch curves and the orientation field using a Swendsen-Wang cut algorithm. Both steps maximize a joint Bayesian posterior probability. The generative model provides a straightforward way for synthesizing realistic hair images and stylistic drawings (rendering) from a sketch graph and a few Gaussian bases. The latter can be either inferred from a real hair image or input (edited) manually using a simple sketching interface. We test our algorithm on a large data set of hair images with diverse hair styles. Analysis, synthesis, and rendering results are reported in the experiments.

Algorithms↗
PubMed2005

Monte Carlo calibration of avalanches described as Coulomb fluid flows.

The idea that snow avalanches might behave as granular flows, and thus be described as Coulomb fluid flows, came up very early in the scientific study of avalanches, but it is not until recently that field evidence has been provided that demonstrates the reliability of this idea. This paper aims to specify the bulk frictional behaviour of snow avalanches by seeking a universal friction law. Since the bulk friction coefficient cannot be measured directly in the field, the friction coefficient must be calibrated by adjusting the model outputs to closely match the recorded data. Field data are readily available but are of poor quality and accuracy. We used Bayesian inference techniques to specify the model uncertainty relative to data uncertainty and to robustly and efficiently solve the inverse problem. A sample of 173 events taken from seven paths in the French Alps was used. The first analysis showed that the friction coefficient behaved as a random variable with a smooth and bell-shaped empirical distribution function. Evidence was provided that the friction coefficient varied with the avalanche volume, but any attempt to adjust a one-to-one relationship relating friction to volume produced residual errors that could be as large as three times the maximum uncertainty of field data. A tentative universal friction law is proposed: the friction coefficient is a random variable, the distribution of which can be approximated by a normal distribution with a volume-dependent mean.

Complex Mixtures↗
PubMed2002

An evaluation of spatio-temporal models for the estimation of the mortality relative risk from breast cancer in Galicia, Spain.

BACKGROUND: Disease mapping is now a big focus of interest in the area of Public Health, and the geographical distribution of a disease has an important role in understanding its origin or its causes. The purpose of this work is to review and evaluate different techniques to map the mortality risk of a disease in small geographical areas. METHODS: Three different methods have been studied. The first one is a classical approach consisting of mapping SMRs, which are maximum likelihood estimates of the relative risk under a Poisson model of death counts. In a second step we consider Poisson and negative binomial regression to fit the rates and finally we use a Bayesian approach that assumes a hierarchical model where the death counts follow a Poisson distribution conditioned by the prior information. These methods have been applied to the study of geographical variation in female breast cancer mortality from 1976 to 1999 in the districts of Galicia, Spain. RESULTS: Mapping the SMRs using the first method has important drawbacks and there are difficulties to distinguish the mortality pattern. With the second method we achieved some improvements. The Bayesian methodology produces smoother maps with a clear mortality pattern. DISCUSSION: These methods are powerful tools for identifying areas with elevated risk. The Bayesian methodology has many advantages over the other methods that had been analysed in this work.

Adult↗
PubMed2004

A comparative evaluation of wavelet-based methods for hypothesis testing of brain activation maps.

Wavelet-based methods for hypothesis testing are described and their potential for activation mapping of human functional magnetic resonance imaging (fMRI) data is investigated. In this approach, we emphasise convergence between methods of wavelet thresholding or shrinkage and the problem of hypothesis testing in both classical and Bayesian contexts. Specifically, our interest will be focused on the trade-off between type I probability error control and power dissipation, estimated by the area under the ROC curve. We describe a technique for controlling the false discovery rate at an arbitrary level of error in testing multiple wavelet coefficients generated by a 2D discrete wavelet transform (DWT) of spatial maps of fMRI time series statistics. We also describe and apply change-point detection with recursive hypothesis testing methods that can be used to define a threshold unique to each level and orientation of the 2D-DWT, and Bayesian methods, incorporating a formal model for the anticipated sparseness of wavelet coefficients representing the signal or true image. The sensitivity and type I error control of these algorithms are comparatively evaluated by analysis of "null" images (acquired with the subject at rest) and an experimental data set acquired from five normal volunteers during an event-related finger movement task. We show that all three wavelet-based algorithms have good type I error control (the FDR method being most conservative) and generate plausible brain activation maps (the Bayesian method being most powerful). We also generalise the formal connection between wavelet-based methods for simultaneous multiresolution denoising/hypothesis testing and methods based on monoresolution Gaussian smoothing followed by statistical testing of brain activation maps.

Algorithms↗
PubMedDate not supplied

A model for case assessment and interpretation.

The authors describe a new approach to decision-making in an operational forensic science organization based on a model, embodying the principles of Bayesian inference, which has been developed through workshops run within the Forensic Science Service for forensic science practitioners. Issues which arise from the idea of pre-assessment of cases are explored by means of a case example.

Bayes Theorem↗
PubMedDate not supplied

Analysis of prevalence of presumed inherited eye diseases in Entlebucher Mountain Dogs.

We analyzed the prevalence of the presumed inherited eye diseases (PIED) noncongenital cataract and progressive retinal atrophy in the Entlebucher Mountain Dog for systematic environmental influences and the additive genetic variation. Multivariate linear animal models using residual maximum likelihood methods and multivariate threshold animal models using Gibbs sampling in Bayesian analyses were used to estimate variance and covariance components. Data were obtained from the kennel club for Swiss Mountain Dog breeds in Germany. PIED were recorded using the standardized protocols of the Dortmunder Kreis, the German panel of the European Eye Scheme for Diagnosis of Inherited Eye Diseases in Animals (DOK). The material included 515 Entlebucher Mountain Dogs from 344 litters at 77 different kennels. Veterinary diagnoses for PIED were from the years 1981-2001. Pedigree information was available for up to nine generations. The multivariate animal model regarded the fixed effects of sex, birth year, experience of the veterinary ophthalmologist, litter size, percentage of examined dogs per litter, inbreeding coefficient and age at examination. The common environment of the litter and the additive genetic effect of the animal were taken into account as randomly distributed effects. The heritability estimates for PIED in the Entlebucher Mountain Dog were h2=0.15+/-0.06 (noncongenital cataract), and h2=0.34+/-0.08 (progressive retinal atrophy) in the linear model and h2=0.32+/-0.05 (noncongenital cataract) and h2=0.59+/-0.03 (progessive retinal atrophy) in the threshold model. The additive genetic correlation between noncongenital cataract and progressive retinal atrophy was moderately positive (r(g)=0.54+/-0.08) in the threshold model. The number of examinations performed by the veterinary ophthalmologists was associated with slightly higher heritabilities for noncongenital cataract and considerably higher heritabilities for progressive retinal atrophy. The investigated PIED in the Entlebucher Mountain Dog are genetically influenced and the size of the genetic parameters estimated may be sensitive to the accuracy of the diagnosis and how the data were collected.

Animals↗
PubMed2006

A general bilinear model to describe growth or decline time profiles.

Linear models are widely used because of their unrivaled simplicity, but they cannot be applied for data that have a turning-or rate-change-point, even if the data show good linearity sufficiently far from this point. To describe such bilinear-type data, a completely generalized version of a linearized biexponential model (LinBiExp) is proposed here to make possible smooth and fully parametrizable transitions between two linear segments while still maintaining a clear connection with the linear models. Applications and brief conclusions are presented for various time profiles of biological and medical interest including growth profiles, such as those of human stature, agricultural crops and fruits, multicellular tumor spheroids, single fission yeast cells, or even labor productivity, and decline profiles, such as age-effects on cognition in patients who develop dementia and lactation yields in dairy cattle. In all these cases, quantitative model selection criteria such as the Akaike and the Schwartz Bayesian information criteria indicated the superiority of the bilinear model compared to adequate less parametrized alternatives such as linear, parabolic, exponential, or classical growth (e.g., logistic, Gompertz, Weibull, and Richards) models. LinBiExp provides a versatile and useful five-parameter bilinear functional form that is convenient to implement, is suitable for full optimization, and uses intuitive and easily interpretable parameters.

Adolescent↗
PubMed2005

Genetic analysis of age at first service, return rate, litter size, and weaning-to-first service interval of gilts and sows.

The aim of this study was to estimate genetic parameters of seven traits related to sow reproductive performance. Data on all Norwegian Landrace pigs (NL) born in nucleus herds and raised in nucleus or multiplying herds from 1990 to 2000 were extracted from the Norwegian national recording scheme. Reproductive traits investigated were age at first service (AFS), return rate in gilts (RRg), age at first farrowing (AFF), live-born piglets in the first litter (NBA1), interval from weaning to first service after first litter (WTS1), return rate after first litter (RR1), live-born piglets in the second litter (NBA2), and interval from weaning to first service after second litter (WTS2). After editing, the data set comprised 12,583 to 56,042 records, depending on the trait. A mixed linear and a joint linear threshold animal model were used to estimate (co)variance components. A full Bayesian approach via Gibbs sampling was adopted. The statistical model used for analysis included contemporary groups of herd-year (-season), purebred or crossbred litter, single or double insemination, mating type, parity in which the animal was born, a regression on lactation length, and an additive genetic effect. Neither the estimated heritabilities nor the genetic correlations differed much between the two approaches, but there was a tendency for higher genetic correlations using the joint linear threshold model approach. Average heritabilities were as follows: AFS = 0.31; RRg = 0.03; RR1 = 0.02; NBA1 = 0.12; NBA2 = 0.14; WTS1 = 0.08; and WTS2 = 0.03. The highest genetic correlations were estimated between NBA1 and NBA2 (r(g) = 0.95), RR1 and WTS1 (r(g) = 0.93), and between WTS1 and WTS2 (r(g) = 0.78). The estimated genetic correlation between NBA and WTS were close to zero. Selection for increased NBA will slightly increase AFS and reduce the probability of a return. Selection for decreased AFS will have a favorable effect on WTS intervals; however, selection for decreased AFS seems to have an unfavorable effect on return rate both on gilts and sows. Conversely, selection for decreased WTS intervals will reduce the probability of a return. Potential selection candidates to include in a multivariate fertility index are AFS, NBA, and WTS1. Due to the low heritability and low, but favorable, genetic correlations to NBA and WTS, RR is not recommended as a selection candidate.

Age Factors↗
PubMed2000

Random effects in censored ordinal regression: latent structure and Bayesian approach.

This paper discusses random effects in censored ordinal regression and presents a Gibbs sampling approach to fit the regression model. A latent structure and its corresponding Bayesian formulation are introduced to effectively deal with heterogeneous and censored ordinal observations. This work is motivated by the need to analyze interval-censored ordinal data from multiple studies in toxicological risk assessment. Application of our methodology to the data offers further support to the conclusions developed earlier using GEE methods yet provides additional insight into the uncertainty levels of the risk estimates.

Animals↗
PubMed1993

Model observers for assessment of image quality.

Image quality can be defined objectively in terms of the performance of some "observer" (either a human or a mathematical model) for some task of practical interest. If the end user of the image will be a human, model observers are used to predict the task performance of the human, as measured by psychophysical studies, and hence to serve as the basis for optimization of image quality. In this paper, we consider the task of detection of a weak signal in a noisy image. The mathematical observers considered include the ideal Bayesian, the nonprewhitening matched filter, a model based on linear-discriminant analysis and referred to as the Hotelling observer, and the Hotelling and Bayesian observers modified to account for the spatial-frequency-selective channels in the human visual system. The theory behind these observer models is briefly reviewed, and several psychophysical studies relating to the choice among them are summarized. Only the Hotelling model with channels is mathematically tractable in all cases considered here and capable of accounting for all of these data. This model requires no adjustment of parameters to fit the data and is relatively insensitive to the details of the channel mechanism. We therefore suggest it as a useful model observer for the purpose of assessing and optimizing image quality with respect to simple detection tasks.

Discrimination, Psychological↗
PubMedDate not supplied

Large-sample Bayesian posterior distributions for probabilistic sensitivity analysis.

In probabilistic sensitivity analyses, analysts assign probability distributions to uncertain model parameters and use Monte Carlo simulation to estimate the sensitivity of model results to parameter uncertainty. The authors present Bayesian methods for constructing large-sample approximate posterior distributions for probabilities, rates, and relative effect parameters, for both controlled and uncontrolled studies, and discuss how to use these posterior distributions in a probabilistic sensitivity analysis. These results draw on and extend procedures from the literature on large-sample Bayesian posterior distributions and Bayesian random effects meta-analysis. They improve on standard approaches to probabilistic sensitivity analysis by allowing a proper accounting for heterogeneity across studies as well as dependence between control and treatment parameters, while still being simple enough to be carried out on a spreadsheet. The authors apply these methods to conduct a probabilistic sensitivity analysis for a recently published analysis of zidovudine prophylaxis following rapid HIV testing in labor to prevent vertical HIV transmission in pregnant women.

Bayes Theorem↗