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Showing 1 to 20 of 21 for “"Laplace Approximation"”.
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Bayesian Methodology for Missing Data, Model Selection and Hierarchical Spatial Models with Application to Ecological Data
… the posterior model probabilities using the Laplace approximation and an approximation based on the Bayesian Information Criterion (BIC) are explored. It is shown that the Laplace approximation is superior to the BIC based approximation using simulation. Finally, Hierarchical Spatial Linear …
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Statistical Challenges in the Modelling and Analysis of Critical Care Mortality Data
… quicker but less accurate alternative is the Laplace approximation, and extending this technique may lead to a better compromise between speed and accuracy. In this thesis, the Laplace approximation is extended beyond that covered in the literature, and details are provided on how the …
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Parameter Estimation Techniques for Nonlinear Dynamic Models with Limited Data, Process Disturbances and Modeling Errors
… noise variance is known. Then, a Fully-Laplace Approximation Expectation Maximization (FLAEM) algorithm is proposed for simultaneous estimation of model parameters, process disturbance intensities and measurement noise variances in nonlinear SDEs. Finally, a Laplace Approximation Maximum …
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Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks
… In particular, we leverage the linearised Laplace approximation to equip pre-trained neural networks with the uncertainty estimates provided by their tangent linear models. This turns the problem of Bayesian inference in neural networks into one of Bayesian inference in conjugate …
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Estimação clássica e Bayesiana em modelos de sobrevida com fração de cura
… by BFGS as implemented in R (base::optim), Laplace approximation (own implementation) and Gibbs sampling as implemented in Winbugs. We describe the main features of the models used, the estimation methods and the computational aspects. We also discuss how different prior information can …
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Bayesian spatio-temporal modelling, mapping and prediction of disease risk
… πραγματοποιείται με τη μέθοδο Integrated Nested Laplace Approximation (INLA), η οποία είναι κατάλληλη για λανθάνοντα Γκαουσιανά μοντέλα υψηλής διάστασης. Η προτεινόμενη μεθοδολογία εφαρμόζεται σε προσομοιωμένα επιδημιολογικά δεδομένα που αναπαριστούν τη χωρική και χρονική εξέλιξη νόσου στην …
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Variable Selection and Hypothesis Testing for High-Dimensional Models in Mental Health Research
… research. A unified framework is developed using Laplace approximation and Newton–Raphson optimization for efficient estimation of fixed effects, random effects, and variance components. Hypothesis testing is conducted using Wald-based and parametric bootstrap methods, while power and sample size …
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Computational Bayesian inference using low discrepancy sequences
The Integrated Nested Laplace Approximation (INLA) provides fast and accurate Bayesian inference for complex hierarchical models. For INLA, and other deterministic methods, the hyperparameter space is explored and points are laid out in a grid structure. These points are used in some numerical …
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Advances in spatial modeling for complex data with applications to symbolic data and spatial transcriptomics.
… facilitated through the Integrated Nested Laplace Approximation (INLA). A bivariate visualization tool is also developed to aid interpretation of the joint behavior of these features across space. Third, SPHERE (Spatial Poisson Hierarchical modEl with pathway-infoRmed gEne networks) is …
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Models and methods for computationally efficient analysis of large spatial and spatio-temporal data
… large spatial and spatio-temporal data. Several approximation methods to "the big n problem" are reviewed, and an extended autoregressive model, called the EAR model, is proposed as a parsimonious model that accounts for smoothness of a process collected over space. It is an extension of the …
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Classifying Challenging Behaviors in Autism Spectrum Disorder with Neural Document Embeddings
… via a Gaussian Process Classifier fitted with Laplace approximation. This classification model, trained on an 80/20 stratified split of the seven most frequently occurring behaviors in the dataset, produces an accuracy of 82.7%. Through this exploration we demonstrate that the semantic queues …
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Advancements in Degradation Modeling, Uncertainty Quantification and Spatial Variable Selection
… and the approximated loglikelihood based on Laplace approximation. We impose the adaptive elastic net penalty to obtain sparse estimation of parameters and thus to achieve variable selection of important variables. The proposed methods are investigated in simulation studies. We also apply the …
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Semiparametric Regression Methods with Covariate Measurement Error
… semi-Bayesian approach which uses a first order Laplace approximation to marginalize the variable measured with error out of the likelihood. The first model is the matched case-control study for analyzing clustered binary outcomes. We develop low-rank thin plate splines for the case where a …
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Impacts of Ignoring Nested Data Structure in Rasch/IRT Model and Comparison of Different Estimation Methods
… such as Penalized Quasi-Likelihood (PQL), Laplace approximation, and Adaptive Gaussian Quadrature (AGQ), commonly used in HGLM in terms of accuracy and efficiency in estimating parameters. As expected, PQL tended to produce seriously biased item difficulty estimates and ability variance …
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Modelling the spatial and temporal distribution of Bacillus anthracis suitability across Uganda and Kenya: A Bayesian Approach
… discuss the application of the Integrated Nested Laplace Approximation (INLA), a spatial method that can be applied to structurally complex data, to model the suitability of B. anthracis. In Chapter 3, I apply a common conventional algorithm to analyse a 15- year dataset comprising confirmed and …
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Learning and Exploiting Camera Geometry for Computer Vision
… fixed second moment, and there is a generalized Laplace approximation whose result is the mirrored normal-Bingham distribution. This distribution and approximation method are demonstrated by deriving the analytical approximation to the wrapped-normal distribution. Further, it is shown how these …
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Learning to Adapt Neural Networks Across Visual Domains
… is a source-trained model. We propose to use Laplace Approximation to build a probabilistic source model that can quantify the uncertainty in the source model predictions on the target data. The uncertainty is then used as importance weights during the target adaptation process, down-weighting …
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Adaptive robotic search and sampling of sparse natural phenomena
… model is developed using the Integrated Nested Laplace Approximation framework, that enables online inference about expected target hotspots using predicted substrate distributions. Model parameters are learned online to build a prediction over the discrete targets, and the model is integrated …
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Adaptive Robotic Search and Sampling of Sparse Natural Phenomena
… model is developed using the Integrated Nested Laplace Approximation framework, that enables online inference about expected target hotspots using predicted substrate distributions. Model parameters are learned online to build a prediction over the discrete targets, and the model is integrated …
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Time-Varying Coefficient Models for Recurrent Events
… embedded with penalized quasi-likelihood approximation is developed to estimate the model parameters. The third part proposes a Bayesian joint model with time-varying coefficients for multi-type recurrent events. Bayesian penalized splines are used to estimate time-varying coefficients and …
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