Bayesian multiple linear regression
Sparse Bayesian Non-linear Regression for Multiple Onsets Estimation in Non-invasive Cardiac Electrophysiology
... kernel regression based on a sparsity inducing prior on the weight parameters within a Bayesian ...SVM regression or Elastic-Net, it provides a posterior probability of each estimated quantity which ...
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Bayesian Functional Linear Regression with Sparse Step Functions
... Keywords: Bayesian regression, functional data, support estimate, ...functional linear models (Ramsay and Silverman, 2005 ) aim at predicting a scalar y based on covariates x 1 (t), x 2 (t), ...
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Bayesian multivariate linear regression with application to change point models in hydrometeorological variables.
... Published Bayesian Change Point Detection Models [ 6 ] A change point can be defined as the date at which at least one parameter of a statistical model ...of linear regression models [Solow, 1987; ...
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Bayesian multivariate linear regression with application to changepoint models in hydrometeorological variables : cases studies
... • Example 1 : this example was drawn from Rasmussen [2001] and deals with a single shift detection in the St-Lawrence streamflows data at Ogdensbourg, New York. The anal[r] ...
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Nonlinear regression using smooth Bayesian estimation
... Index Terms— Bayesian algorithm, Hamiltonian Monte- Carlo, MCMC, Parameter estimation, Radar altimetry. 1. INTRODUCTION In many applications, the observed data are well described by a nonlinear function of a ...
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Multiclass Sparse Bayesian Regression for fMRI-Based Prediction
... General Linear Model (GLM) fit were performed with the SPM5 software ...a regression problem, in which our goal is to predict a simple scalar factor (size of an ob- ...
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Nonlinear regression using smooth Bayesian estimation
... Index Terms— Bayesian algorithm, Hamiltonian Monte- Carlo, MCMC, Parameter estimation, Radar altimetry. 1. INTRODUCTION In many applications, the observed data are well described by a nonlinear function of a ...
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Unsupervised Bayesian linear unmixing of gene expression microarrays
... Bayesian linear unmixing, traditionally used for hyper- spectral image analysis (see [13] for example), is one of many possible factor analysis methods that could be applied to gene expression ...[15], ...
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Linear regression and learning : contributions to regularization and aggregation methods
... include Bayesian model averaging (batch, stochastic) and random forests (batch, stochastic), as well as robust online aggrega- tion (sequential, deterministic), also known as prediction of individual sequences or ...
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Bayesian Support Vector Regression for traffic speed prediction with error bars
... Patrick Jaillet is with the Laboratory for Information and Decision Systems, MIT, Cambridge, MA, 02139. This technique combines advantages of SVR and Bayesian inference [8]. SVR is highly suitable for traffic ...
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Adaptive multi-class Bayesian sparse regression - An application to brain activity classification
... Preliminary results on real data shows the advantages of our method. The VBK algorithm gives access to highly interpretable loadings maps which are a powerful tool for understanding brain activity. Moreover, the free ...
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Bayesian inverse regression for vascular magnetic resonance fingerprinting
... II. MRF AS AN INVERSE PROBLEM In inverse problems, the overall issue is to provide infor- mation on some parameters of interest x given an observed signal y, using a known direct or forward model that describes how the ...
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Recursion-based multiple changepoint detection in multiple linear regression and application to river streamflows.
... In Bayesian statistics, one is interested in obtaining a statistical distribution for the dates of change and eventually a distribution for the other model ...parameters. Bayesian changepoint analysis ...
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Bayesian dynamic linear models for structural health monitoring
... This paper presents a framework for building, learning and estimating Bayesian Dynamic Linear Models (BDLM). Specifically, the contributions of this paper enable creating models of external effect and ...
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Regularized Bayesian quantile regression.
... In classical QR model, the parameter are estimated by solving the optimization problem 1. Indeed, Koenker and Bassett (1978) show that minimizing the loss function for given sample, leads to the p th regression ...
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Bayesian Nonparametric Inference of Switching Dynamic Linear Models
... a linear dynamical ...switching linear dynamical processes, with roots in the control and econometrics literature, have recently become more prevalent within signal processing [10], ...
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A global homogeneity test for high-dimensional linear regression
... high-dimensional linear regression testing scheme to derive a global test for the equality of Gaussian graphical mod- els inferred under pairs of ...
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Linear regression with stationary errors : the R package slm
... Bertrand Michel LMJL Ecole Centrale Nantes Abstract This paper introduces the R package slm which stands for Stationary Linear Models. The package contains a set of statistical procedures for linear ...
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Bayesian Source Separation of Linear and Linear-quadratic Mixtures Using Truncated Priors
... Received: date / Accepted: date Abstract In this work, we propose a Bayesian source separation method of linear- quadratic (LQ) and linear mixtures. Since our method relies on truncated prior dis- ...
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Non-linear regression algorithms for motor skill acquisition: a comparison
... of linear models, i.e. a weighted sum of local linear models where the weights depend on the input space through Gaussian basis functions, ...Weighted Regression ( LWR ) (Atkeson and Schaal, 1995), ...
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