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[PDF] Top 20 Nonlinear regression using smooth Bayesian estimation

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Nonlinear regression using smooth Bayesian estimation

Nonlinear regression using smooth Bayesian estimation

... Terms— Bayesian algorithm, Hamiltonian Monte- Carlo, MCMC, Parameter estimation, Radar ...a nonlinear function of a vector of parameters ...non-linear regression model. This can be achieved by ... Voir le document complet

6

Nonlinear regression using smooth Bayesian estimation

Nonlinear regression using smooth Bayesian estimation

... new Bayesian strategy for the estima- tion of smooth parameters from nonlinear ...a smooth temporal evolution of the model parame- ters is ...proposed Bayesian model and the ... Voir le document complet

7

Bayesian Estimation of Smooth Altimetric Parameters: Application to Conventional and Delay/Doppler Altimetry

Bayesian Estimation of Smooth Altimetric Parameters: Application to Conventional and Delay/Doppler Altimetry

... posterior using Markov chain Monte Carlo (MCMC) ...good estimation results, and it will be briefly recalled in this ...by using a gradient descent ...by using the Fisher information matrix and ... Voir le document complet

14

Bayesian Regression and Classification Using Gaussian Process Priors Indexed by Probability Density Functions

Bayesian Regression and Classification Using Gaussian Process Priors Indexed by Probability Density Functions

... log-Euclidean, Jensen-Shannon and Wasserstein distances [43, 21, 39, 30, 7]. In this work, we extend this formulation to PDFs space P with the Mat´ ern covariance functions. There is a rich literature on statistical ... Voir le document complet

19

Sparse Bayesian Non-linear Regression for Multiple Onsets Estimation in Non-invasive Cardiac Electrophysiology

Sparse Bayesian Non-linear Regression for Multiple Onsets Estimation in Non-invasive Cardiac Electrophysiology

... ridge regression acts as a blurry mixture of both initializations, not able to separate ...RVM regression performed on each vertex captures the two onset zones, but the resulting activation maps are noisy ... Voir le document complet

10

Iron Grades Estimation Using Bayesian Sequential Simulation - Lalor Case Study.

Iron Grades Estimation Using Bayesian Sequential Simulation - Lalor Case Study.

... reserve estimation and mineral deposit ...by using only a limited number of core data coupled with geophysical and geological information which are distributed on the entire investigated ...used ... Voir le document complet

5

Bayesian conditional Monte Carlo Algorithm for nonlinear time-series state estimation

Bayesian conditional Monte Carlo Algorithm for nonlinear time-series state estimation

... that using local sets of M samples enables us to reduce N and, as an additional side effect, to also reduce the computational time due to the propagation of the main set of N particles (in particular during the ... Voir le document complet

15

Bayesian Estimation of Smooth Altimetric Parameters: Application to Conventional and Delay/Doppler Altimetry

Bayesian Estimation of Smooth Altimetric Parameters: Application to Conventional and Delay/Doppler Altimetry

... parameters using the LS algorithm with the CD and HMC algorithms when considering both CA and DDA ...the estimation results of SWH and of the sea surface height anomaly (SSHA) which are the most important ... Voir le document complet

15

Sparse Bayesian binary logistic regression using the split-and-augmented Gibbs sampler

Sparse Bayesian binary logistic regression using the split-and-augmented Gibbs sampler

... Pierre Chainais University of Lille, CNRS, Centrale Lille UMR 9189 - CRIStAL, Lille, France applied to re-write the logistic distribution. For instance, [11] repre- sented this distribution as a normal-scale mixture and ... Voir le document complet

8

Bayesian multifractal analysis of multi-temporal images using smooth priors

Bayesian multifractal analysis of multi-temporal images using smooth priors

... appropriate regression weights [4, 9, ...improve estimation performance are reported in the ...recently, Bayesian estima- tors for c 2 have been proposed [13, ... Voir le document complet

7

Nonlinear functional regression: a functional RKHS approach

Nonlinear functional regression: a functional RKHS approach

... concepts: smooth- ing (Ramsay and Silverman, 2005), functional analy- sis (Ferraty and Vieu, 2006) and stochastic process (He et ...2007). Using functional anal- ysis (Rudin, 1991), observational unit is ... Voir le document complet

8

Classification of linear and nonlinear modulations using Bayesian methods

Classification of linear and nonlinear modulations using Bayesian methods

... The key idea of the MCMC plug-in classifier is to replace the unknown parameters in the likelihood of the observed data by their estimates. These values are obtained from averaging samples drawn by MH algorithm which is ... Voir le document complet

151

Archimedean copula estimation using Bayesian splines smoothing techniques

Archimedean copula estimation using Bayesian splines smoothing techniques

... derivative using B-splines is proposed and the associated parameters estimated using Markov chains Monte-Carlo ...The estimation is reasonably quick. The fitted generator is smooth and ...the ... Voir le document complet

22

Efficient Estimation of Multidimensional Regression Model using Multilayer Perceptrons

Efficient Estimation of Multidimensional Regression Model using Multilayer Perceptrons

... This work concerns the estimation of multidimensional nonlinear re- gression models using multilayer perceptrons (MLPs). The main problem with such models is that we need to know the covariance ... Voir le document complet

17

Bayesian multifractal analysis of multi-temporal images using smooth priors

Bayesian multifractal analysis of multi-temporal images using smooth priors

... w j d Var [ln l(j, k)] (3) where w j are appropriate regression weights [4, 9, 11]. This esti- mator is widely used and known to provide relatively poor perfor- mance, in particular for small image size. Different ... Voir le document complet

6

Sparse Bayesian binary logistic regression using the split-and-augmented Gibbs sampler

Sparse Bayesian binary logistic regression using the split-and-augmented Gibbs sampler

... Pierre Chainais University of Lille, CNRS, Centrale Lille UMR 9189 - CRIStAL, Lille, France applied to re-write the logistic distribution. For instance, [11] repre- sented this distribution as a normal-scale mixture and ... Voir le document complet

7

Bayesian estimation of bandwidth in semiparametric kernel estimation of unknown probability mass and regression functions of count data

Bayesian estimation of bandwidth in semiparametric kernel estimation of unknown probability mass and regression functions of count data

... Recently, Bayesian approaches using binomial kernel in equation (1) have been proposed as an alternative to cross-validation procedure for bandwidth selections but only for nonparametric estimations of pmf ... Voir le document complet

19

Bayesian Functional Linear Regression with Sparse Step Functions

Bayesian Functional Linear Regression with Sparse Step Functions

... Bayesian Functional Linear Regression with Sparse Step Functions Paul-Marie Grollemund ∗ , † , Christophe Abraham ‡ , Me¨ıli Baragatti § , and Pierre Pudlo ¶ ...linear regression model is a common ... Voir le document complet

26

Cooperative localization of marine vehicles using nonlinear state estimation

Cooperative localization of marine vehicles using nonlinear state estimation

... II. AUV C OOPERATIVE L OCALIZATION W ITH A S INGLE S URFACE V EHICLE A. Problem Definition Within the field of cooperative marine robotics the coop- erative localization problem has been developed as follows: acoustic ... Voir le document complet

7

Nonparametric estimation of regression level sets using kernel plug-in estimator

Nonparametric estimation of regression level sets using kernel plug-in estimator

... the estimation of the level sets of the regression function has not been widely ...estimators using smoothness assumptions on the boundary of the level ...estimator using the kernel estimator ... Voir le document complet

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