Search results for "Posterior probability"
showing 10 items of 58 documents
Imputation of posterior linkage probability relations reveals a significant influence of structural 3D constraints on linkage disequilibrium
2018
Genetic association studies have become increasingly important in unraveling the genetics of diseases or complex traits. Despite their value for modern genetics, conflicting conclusions often arise through the difficulty of confirming and replicating experimental results. We argue that this problem is largely based on the application of statistical relation measures that are not appropriate for genomic data analysis and demonstrate that the standard measures used for Genome-wide association studies or genomics linkage analysis bear a statistic bias. This may come from the violation of underlying assumptions (such as independence or stationarity) as well as from other conceptual limitations …
A supervised learning framework of statistical shape and probability priors for automatic prostate segmentation in ultrasound images
2013
Prostate segmentation aids in prostate volume estimation, multi-modal image registration, and to create patient specific anatomical models for surgical planning and image guided biopsies. However, manual segmentation is time consuming and suffers from inter-and intra-observer variabilities. Low contrast images of trans rectal ultrasound and presence of imaging artifacts like speckle, micro-calcifications, and shadow regions hinder computer aided automatic or semi-automatic prostate segmentation. In this paper, we propose a prostate segmentation approach based on building multiple mean parametric models derived from principal component analysis of shape and posterior probabilities in a multi…
Spectral clustering of shape and probability prior models for automatic prostate segmentation.
2013
Imaging artifacts in Transrectal Ultrasound (TRUS) images and inter-patient variations in prostate shape and size challenge computer-aided automatic or semi-automatic segmentation of the prostate. In this paper, we propose to use multiple mean parametric models derived from principal component analysis (PCA) of shape and posterior probability information to segment the prostate. In contrast to traditional statistical models of shape and intensity priors, we use posterior probability of the prostate region determined from random forest classification to build, initialize and propagate our model. Multiple mean models derived from spectral clustering of combined shape and appearance parameters…
Bayesian forecasting with the Holt–Winters model
2010
Exponential smoothing methods are widely used as forecasting techniques in inventory systems and business planning, where reliable prediction intervals are also required for a large number of series. This paper describes a Bayesian forecasting approach based on the Holt–Winters model, which allows obtaining accurate prediction intervals. We show how to build them incorporating the uncertainty due to the smoothing unknowns using a linear heteroscedastic model. That linear formulation simplifies obtaining the posterior distribution on the unknowns; a random sample from such posterior, which is not analytical, is provided using an acceptance sampling procedure and a Monte Carlo approach gives …
Statistical Shape and Probability Prior Model for Automatic Prostate Segmentation
2011
International audience; Accurate prostate segmentation in Trans Rectal Ultra Sound (TRUS) images is an important step in different clinical applications. However, the development of computer aided automatic prostate segmentation in TRUS images is a challenging task due to low contrast, heterogeneous intensity distribution inside the prostate region, imaging artifacts like shadow, and speckle. Significant variations in prostate shape, size and contrast between the datasets pose further challenges to achieve an accurate segmentation. In this paper we propose to use graph cuts in a Bayesian framework for automatic initialization and propagate multiple mean parametric models derived from princi…
Bayesian estimation of edge orientations in junctions
1999
Abstract Junctions, defined as those points of an image where two or more edges meet, play a significant role in many computer vision applications. Junction detection is a widely treated problem, and some detectors can provide even the directions of the edges that meet in a junction. The main objective of this paper is the precise estimation of such directions. It is supposed that the junction point has been previously found by some detector. Also, it is assumed that samples, possibly noisy, of orientations of the edges found in a circular window surrounding the point are available. A mixture of von Mises distributions is assumed for these data, and then a Bayesian methodology is applied to…
On the checking of g-coherence of conditional probability bounds
2003
We illustrate an approach to uncertain knowledge based on lower conditional probability bounds. We exploit the coherence principle of de Finetti and a related notion of generalized coherence (g-coherence), which is equivalent to the "avoiding uniform loss" property introduced by Walley for lower and upper probabilities. Based on the additive structure of random gains, we define suitable notions of non relevant gains and of basic sets of variables. Exploiting them, the linear systems in our algorithms can work with reduced sets of variables and/or constraints. In this paper, we illustrate the notions of non relevant gain and of basic set by examining several cases of imprecise assessments d…
Forecasting correlated time series with exponential smoothing models
2011
Abstract This paper presents the Bayesian analysis of a general multivariate exponential smoothing model that allows us to forecast time series jointly, subject to correlated random disturbances. The general multivariate model, which can be formulated as a seemingly unrelated regression model, includes the previously studied homogeneous multivariate Holt-Winters’ model as a special case when all of the univariate series share a common structure. MCMC simulation techniques are required in order to approach the non-analytically tractable posterior distribution of the model parameters. The predictive distribution is then estimated using Monte Carlo integration. A Bayesian model selection crite…
Updated determination of the solar neutrino fluxes from solar neutrino data
2016
Journal of High Energy Physics 2016.3 (2016): 132 reproduced by permission of Scuola Internazionale Superiore di Studi Avanzati (SISSA)
An Ordinal Joint Model for Breast Cancer
2017
We propose a Bayesian joint model to analyze the association between longitudinal measurements of an ordinal marker and time to a relevant event. The longitudinal process is defined in terms of a proportional-odds cumulative logit model and the time-to-event process through a left-truncated Cox proportional hazards model with information of the longitudinal marker and baseline covariates. Both longitudinal and survival processes are connected by a common vector of random effects.