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See detailBayesian integration of external information into the single step approach for genomically enhanced prediction of breeding values
Vandenplas, Jérémie ULg; Misztal, Ignacy; Faux, Pierre ULg et al

in Journal of Dairy Science (2012), 95(Supplement 2),

Detailed reference viewed: 37 (7 ULg)
See detailBayesian inversion for induced responses
Mattout, Jérémie; Phillips, Christophe ULg; Daunizeau, Jean et al

in Friston, Karl; Ashburner, John; Kiebel, Stefan (Eds.) et al Statistical Parametric Mapping: the analysis of functional brain images (2007)

The book Statistical Parametric Mapping: The Analysis of Functional Brain Images (2007) provides the background and methodology for the analysis of all types of brain imaging data, from functional ... [more ▼]

The book Statistical Parametric Mapping: The Analysis of Functional Brain Images (2007) provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically, "Statistical Parametric Mapping" provides a widely accepted conceptual framework which allows treatment of all these different modalities. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. The material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. [less ▲]

Detailed reference viewed: 11 (0 ULg)
See detailBayesian inversion of EEG models
Mattout, Jérémie; Phillips, Christophe ULg; Daunizeau, Jean et al

in Friston, Karl; Ashburner, John; Kiebel, Stefan (Eds.) et al Statistical Parametric Mapping: the analysis of functional brain images (2007)

The book Statistical Parametric Mapping: The Analysis of Functional Brain Images (2007) provides the background and methodology for the analysis of all types of brain imaging data, from functional ... [more ▼]

The book Statistical Parametric Mapping: The Analysis of Functional Brain Images (2007) provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically, "Statistical Parametric Mapping" provides a widely accepted conceptual framework which allows treatment of all these different modalities. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. The material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. [less ▲]

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See detailBayesian methods for predicting and modelling winter wheat biomass
Mansouri, Majdi ULg; Dumont, Benjamin ULg; Destain, Marie-France ULg

Poster (2014, February)

The objectives of this paper are threefold. The first objective is to propose to use an Improved Particle Filtering (IPF) based on minimizing Kullback-Leibler divergence for crop models' predictions. The ... [more ▼]

The objectives of this paper are threefold. The first objective is to propose to use an Improved Particle Filtering (IPF) based on minimizing Kullback-Leibler divergence for crop models' predictions. The performances of the proposed technique are compared with those of the conventional Particle Filtering (PF) for improving nonlinear crop model predictions. The main novelty of this task is to develop a Bayesian algorithm for nonlinear and non-Gaussian state and parameter estimation with better proposal distribution. The second objective is to investigate the effects of practical challenges on the performances of state estimation algorithms PF and IPF. Such practical challenges include (i) the effect of measurement noise on the estimation performances and (ii) the number of states and parameters to be estimated. The third objective is to use the state estimation techniques PF and IPF for updating prediction of nonlinear crop model in order to predict winter wheat biomass. PF and IPF are applied at a dynamic crop model with the aim to predict a state variable, namely the winter wheat biomass, and to estimate several model parameters. Furthermore, the effect of measurement noise (e.g., different signal-to-noise ratios) on the performances of PF and IPF is investigated. The results of the comparative studies show that the IPF provides a significant improvement over the PF because, unlike the PF which depends on the choice of sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of the sampling distribution, which also accounts for the observed data. [less ▲]

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See detailBayesian methods for predicting LAI and soil moisture
Mansouri, Majdi ULg; Dumont, Benjamin ULg; Destain, Marie-France ULg

in Proceedings of the 11th International Conference on Precision Agriculture (2012)

The prediction errors of crop models are often important due to uncertainties in the estimates of initial values of the states, in parameters, and in equations. The measurements needed to run the model ... [more ▼]

The prediction errors of crop models are often important due to uncertainties in the estimates of initial values of the states, in parameters, and in equations. The measurements needed to run the model are sometimes not numerous or known with some uncertainty. In this paper, two Bayesian filtering methods were used to update the state variable values predicted by MiniSTICS model. The chosen state variates were the LAI (Leaf Area Index) of a wheat crop (Triticum aestivum L.) and the corresponding moisture content of two soil layers (0-20 cm and 30-50 cm). These state variates were estimated simultaneously with several parameters. The assessed filtering methods were the centralized Particle Filtering (PF) and the Variational Bayesian Filtering (VF). The former is known to be sensitive to the number of particles while the latter yields an optimal choice of the sampling distribution over the state variable by minimizing the Kullback-Leibler divergence. In fact, variational calculus leads to a simple Gaussian sampling distribution whose parameters (estimated iteratively) depends on the observed data. On basis of a case study, the VF method was found more efficient than the PF method. Indeed, with the VF, the Root Mean Square Error (RMSE) of the three estimated states was smaller and the convergence of the all parameters was ensured. [less ▲]

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See detailBayesian methods for predicting LAI and soil water content
Mansouri, Majdi ULg; Dumont, Benjamin ULg; Leemans, Vincent ULg et al

in Precision Agriculture (2014), 15(2), 184-201

LAI of winter wheat (Triticum aestivum L.) and soil water content of the topsoil (200 mm) and of the subsoil (500 mm) were considered as state variables of a dynamic soil-crop system. This system was ... [more ▼]

LAI of winter wheat (Triticum aestivum L.) and soil water content of the topsoil (200 mm) and of the subsoil (500 mm) were considered as state variables of a dynamic soil-crop system. This system was assumed to progress according to a Bayesian probabilistic state space model, in which real values of LAI and soil water content were daily introduced in order to correct the model trajectory and reach better future evolution. The chosen crop model was mini STICS which can reduce the computing and execution times while ensuring the robustness of data processing and estimation. To predict simultaneously state variables and model parameters in this non-linear environment, three techniques were used: Extended Kalman Filtering (EKF), Particle Filtering (PF), and Variational Filtering (VF). The significantly improved performance of the VF method when compared to EKF and PF is demonstrated. The variational filter has a low computational complexity and the convergence speed of states and parameters estimation can be adjusted independently. Detailed case studies demonstrated that the root mean square error (RMSE) of the three estimated states (LAI and soil water content of two soil layers) was smaller and that the convergence of all considered parameters was ensured when using VF. Assimilating measurements in a crop model allows accurate prediction of LAI and soil water content at a local scale. As these biophysical properties are key parameters in the crop-plant system characterization, the system has the potential to be used in precision farming to aid farmers and decision makers in developing strategies for site-specific management of inputs, such as fertilizers and water irrigation. [less ▲]

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See detailBayesian model screening for the identification of nonlinear mechanical structures
Kerschen, Gaëtan ULg; Golinval, Jean-Claude ULg; Hemez, F. M.

in Journal of Vibration and Acoustics-Transactions of the Asme (2003), 125(3), 389-397

The development of techniques for identification and updating of nonlinear mechanical structures has received increasing attention in recent years. In practical situations, there is not necessarily a ... [more ▼]

The development of techniques for identification and updating of nonlinear mechanical structures has received increasing attention in recent years. In practical situations, there is not necessarily a priori knowledge about the nonlinearity. This suggests the need for strategies that allow inference of useful information from the data. The present study proposes an algorithm based on a Bayesian inference approach for giving insight into the form of the nonlinearity. A family of parametric models is defined to represent the nonlinear response of a system and the selection algorithm estimates the likelihood that each member of the family is appropriate. The (unknown) probability density function of the family of models is explored using a simple variant of the Markov Chain Monte Carlo sampling technique. This technique offers the advantage that the nature of the underlying statistical distribution need not be assumed a priori. Enough samples are drawn to guarantee that the empirical distribution approximates the true but unknown distribution to the desired level of accuracy. It provides an indication of which models are the most appropriate to represent the nonlinearity and their respective goodness-of-fit to the data. The methodology is illustrated using two examples, one of which comes from experimental data. [less ▲]

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See detailBayesian ODE-penalized B-spline model with Gaussian mixture as error distribution
Jaeger, Jonathan ULg; Lambert, Philippe ULg

Scientific conference (2012, July 18)

In the standard Bayesian ODE-penalized B-spline approach, it is assumed that the error distribution is homogeneous Gaussian. But, in many applications, the normal assumption for the error distribution is ... [more ▼]

In the standard Bayesian ODE-penalized B-spline approach, it is assumed that the error distribution is homogeneous Gaussian. But, in many applications, the normal assumption for the error distribution is not a realistic choice. The goal of this paper is to extend the standard Bayesian ODE-penalized B-spline approach to settings where the error term distribution can be described using a mixture of normals. [less ▲]

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See detailBayesian P-spline estimation in hierarchical models specified by systems of affine differential equations
Jaeger, Jonathan ULg; Lambert, Philippe ULg

in Statistical Modelling : An International Journal (2013), 13

Ordinary differential equations (ODEs) are widely used to model physical, chemical and biological processes. Current methods for parameter estimation can be computationally intensive and/or not suitable ... [more ▼]

Ordinary differential equations (ODEs) are widely used to model physical, chemical and biological processes. Current methods for parameter estimation can be computationally intensive and/or not suitable for inference and prediction. Frequentist approaches based on ODE-penalized smoothing techniques have recently solved part of these drawbacks. A full Bayesian approach based on ODE-penalized B-splines is proposed to jointly estimate ODE parameters and state functions from affine systems of differential equations. Simulations inspired by pharmacokinetic studies show that the proposed method provides comparable results to methods based on explicit solution of the ODEs and outperforms the frequentist ODE-penalized smoothing approach. The basic model is extended to a hierarchical one in order to study cases where several subjects are involved. This Bayesian hierarchical approach is illustrated on real data for the study of perfusion ratio after a femoral artery occlusion. Model selection is feasible through the analysis of the posterior distributions of the ODE adhesion parameters and is illustrated on a real pharmacokinetic dataset. [less ▲]

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See detailBayesian penalized smoothing approaches in models specified using affine differential equations with unknown error distributions
Jaeger, Jonathan ULg; Lambert, Philippe ULg

in Journal of Applied Statistics (in press)

A full Bayesian approach based on ODE-penalized B-splines and penalized Gaussian mixture is proposed to jointly estimate ODE-parameters, state function and error distribution from the observation of some ... [more ▼]

A full Bayesian approach based on ODE-penalized B-splines and penalized Gaussian mixture is proposed to jointly estimate ODE-parameters, state function and error distribution from the observation of some state functions involved in systems of affine differential equations. Simulations inspired by pharmacokinetic studies show that the proposed method provides comparable results to the method based on the standard ODE-penalized B-spline approach (i.e. with the Gaussian error distribution assumption) and outperforms the standard ODE-penalized B-splines when the distribution is not Gaussian. This methodology is illustrated on the Theophylline dataset. [less ▲]

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See detailA Bayesian person fit evaluation for polytomous response data
Béland, Sébastien; Hoijtink, Herbert; Raîche, Gilles et al

Conference (2011, July 19)

Studies about Person-fit are generally produced under a frequentist approach. For example, Meijer & Sijtsma (2001) discussed many parametric and non-parametric indexes in their review on this topic ... [more ▼]

Studies about Person-fit are generally produced under a frequentist approach. For example, Meijer & Sijtsma (2001) discussed many parametric and non-parametric indexes in their review on this topic. However, it exists also few papers about the investigation of person-fit in a Bayesian context (e.g. Glas & Meijer, 2003; Van Der Linden & Guo, 2008). In this talk, we present a new method based on the evaluation of informative hypotheses using the Bayes factor. This approach is non-parametric in nature and can be applied to a large variety of situations and many types of data. Here, we focus on the use of Bayesian person-fit methods that can be used with polytomous response data. This presentation is divided in two sections. First, we present the technical aspects of this approach by discussing some hypotheses of interest, the nature of the prior and the nature of the posterior. Second, we present results from a real data matrix. The first analysis shows that Bayesian person-fit evaluation is efficient and can be easily applied to small data matrices. [less ▲]

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See detailA BAYESIAN PROBABILITY CRITERION TO ASSESS ANALYTICAL RESULTS RELIABILITY
Rozet, Eric ULg; Lebrun, Pierre ULg; Boulanger, B et al

Conference (2013, May 21)

In pharmaceutical and biomedical industries, quantitative analytical methods such as HPLC play a key role. Indeed, the analytical results obtained from them are used to make crucial decisions such as the ... [more ▼]

In pharmaceutical and biomedical industries, quantitative analytical methods such as HPLC play a key role. Indeed, the analytical results obtained from them are used to make crucial decisions such as the release of batches of drugs, the evaluation of safety and efficacy of new drug candidates or the monitoring of patients health. Prior to their routine use, analytical methods are submitted to a stringent validation study [1] where they have to demonstrate that they are fit for their final purpose, i.e. providing accurate results: where is the analytical result, is the theoretical unknown true concentration of analyte in the sample analyzed and a regulatory acceptance limit. Typically this demonstration is made by either providing point estimates of systematic error (bias) and random error (variance) or sometimes by providing interval estimates of these statistical parameters at several well defined concentration levels of the target analyte [2]. They are then compared to maximum acceptable levels. More recently, tolerance intervals approaches have been proposed that are evaluated in a similar way at these key concentration levels [3]. However none of these decision approaches allow knowing the probability to obtain accurate results over the whole concentration range of interest: is a vector of parameters and Pmin is a minimum reliability probability. Frequentist approximations have been proposed to estimate this probability but only at the concentration levels experimentally tested [4,5]. In this work, a linear hierarchical Bayesian approach is proposed. It takes into account the potential random characteristic of the slope and intercept observed from one analytical run to the other, but it also integrates the possible covariance between the parameters. Additionally, heteroscedasticity of the residual variance over the concentration range investigated is taken into account. A situation regularly observed in practice. Finally a reliability profile for the whole concentration range studied is obtained using MCMC sampling. This profile provides the probability (Prel) to obtain accurate results over the full concentration range investigated. This profile is then compared to a minimum reliability probability (Pmin) that will define the valid concentration range of the analytical method. The usefulness of this approach is illustrated through the validation of a bioanalytical method and also compared with one concentration level at a time frequentist approaches [4,5]. [1] International Conference on Harmonization (ICH) of Technical Requirements for registration of Pharmaceuticals for Human Use Topic Q2 (R1): Validation of Analytical Procedures: Text and Methodology, Geneva, 2005. [2] A. Bouabidi and al., J. Chromatogr. A, 1217 (2010) 3180. [3] Ph. Hubert and al., J. Pharm. Biomed. Anal., 36 (2004) 579. [4] W. Dewé and al., Chemometr. Intell. Lab. Syst. 85 (2007) 262. [5] B. Govaerts and al., Qual. Reliab. Engng. Int. 24 (2008) 667. [less ▲]

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See detailBayesian proportional hazards model with time varying regression coefficients: a penalized Poisson regression approach
Lambert, Philippe ULg; Eilers, Paul H.C.

in Statistics in Medicine (2005), 24

One can fruitfully approach survival problems without covariates in an actuarial way. In narrow time bins, the number of people at risk is counted together with the number of events. The relationship ... [more ▼]

One can fruitfully approach survival problems without covariates in an actuarial way. In narrow time bins, the number of people at risk is counted together with the number of events. The relationship between time and probability of an event can then be estimated with a parametric or semi-parametric model. The number of events observed in each bin is described using a Poisson distribution with the log mean speci ed using a exible penalized B-splines model with a large number of equidistant knots. Regression on pertinent covariates can easily be performed using the same log-linear model, leading to the classical proportional hazard model. We propose to extend that model by allowing the regression coe cients to vary in a smooth way with time. Penalized B-splines models will be proposed for each of these coe cients. We show how the regression parameters and the penalty weights can be estimated e ciently using Bayesian inference tools based on the Metropolis-adjusted Langevin algorithm. [less ▲]

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See detailBazooka est-il soluble dans l'art contemporain ?
Dejasse, Erwin ULg

in 9e Art : Les Cahiers du Musée de la Bande Dessinée (2006), 12

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See detailBCL-3 degradation involves its polyubiquitination through a FBW7-independent pathway and its binding to the proteasome subunit PSMB1.
Keutgens, Aurore ULg; Zhang-Shao, Xin ULg; Shostak, Kateryna ULg et al

in Journal of Biological Chemistry (2010), 285(33), 2583125840

The oncogenic protein BCL-3 activates or represses gene transcription through binding with the NF-kappaB proteins p50 and p52 and is degraded through a phospho- and GSK3-dependent pathway. However, the ... [more ▼]

The oncogenic protein BCL-3 activates or represses gene transcription through binding with the NF-kappaB proteins p50 and p52 and is degraded through a phospho- and GSK3-dependent pathway. However, the mechanisms underlying its degradation remain poorly understood. Yeast-two-hybrid analysis led to the identification of the proteasome subunit PSMB1 as a BCL-3-associated protein. The binding of BCL-3 to PSMB1 is required for its degradation through the proteasome. Indeed, PSMB1-depleted cells are defective in degrading polyubiquitinated BCL-3. The N-terminal part of BCL-3 includes lysines 13 and 26 required for the K48-linked polyubiquitination of BCL-3. Moreover, the E3 ligase FBW7 known to polyubiquitinate a variety of substrates phosphorylated by GSK3 is dispensable for BCL-3 degradation. Thus, our data defined an unique motif of BCL-3 that is needed for its recruitment to the proteasome and identified PSMB1 as a key protein required for the proteasome-mediated degradation of a nuclear and oncogenic IkappaB protein. [less ▲]

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See detailBCOR analysis in patients with OFCD and Lenz microphthalmia syndromes, mental retardation with ocular anomalies, and cardiac laterality defects.
Hilton, Emma; Johnston, Jennifer; Whalen, Sandra et al

in European Journal of Human Genetics (2009), 17(10), 1325-35

Oculofaciocardiodental (OFCD) and Lenz microphthalmia syndromes form part of a spectrum of X-linked microphthalmia disorders characterized by ocular, dental, cardiac and skeletal anomalies and mental ... [more ▼]

Oculofaciocardiodental (OFCD) and Lenz microphthalmia syndromes form part of a spectrum of X-linked microphthalmia disorders characterized by ocular, dental, cardiac and skeletal anomalies and mental retardation. The two syndromes are allelic, caused by mutations in the BCL-6 corepressor gene (BCOR). To extend the series of phenotypes associated with pathogenic mutations in BCOR, we sequenced the BCOR gene in patients with (1) OFCD syndrome, (2) putative X-linked ('Lenz') microphthalmia syndrome, (3) isolated ocular defects and (4) laterality phenotypes. We present a new cohort of females with OFCD syndrome and null mutations in BCOR, supporting the hypothesis that BCOR is the sole molecular cause of this syndrome. We identify for the first time mosaic BCOR mutations in two females with OFCD syndrome and one apparently asymptomatic female. We present a female diagnosed with isolated ocular defects and identify minor features of OFCD syndrome, suggesting that OFCD syndrome may be mild and underdiagnosed. We have sequenced a cohort of males diagnosed with putative X-linked microphthalmia and found a mutation, p.P85L, in a single case, suggesting that BCOR mutations are not a major cause of X-linked microphthalmia in males. The absence of BCOR mutations in a panel of patients with non-specific laterality defects suggests that mutations in BCOR are not a major cause of isolated heart and laterality defects. Phenotypic analysis of OFCD and Lenz microphthalmia syndromes shows that in addition to the standard diagnostic criteria of congenital cataract, microphthalmia and radiculomegaly, patients should be examined for skeletal defects, particularly radioulnar synostosis, and cardiac/laterality defects. [less ▲]

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See detailLe BCS, une méthode simple à la source de conseils variés : ration, repro et santé
Laloux, Laurent; Bastin, Catherine ULg; Gillon, Alain ULg et al

Poster (2009, February 11)

Detailed reference viewed: 17 (6 ULg)