Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 23 for “"Marginal Likelihood"”.
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Strength out of Weakness: Harnessing Information Gained from the Pair Structure of Composite Marginal Likelihood Estimation
… tool for models estimated using composite marginal likelihood (CML) methods, to detect model misspecifications and enhance model fit. <br /><br /> The second paper demonstrates the use of pairwise CML estimation for gradient-based Lagrange multiplier-type tests, enabling the comparison of …
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Model selection in compositional spaces
… infer latent components and estimate predictive likelihood for nearly 2500 model structures using a small toolbox of reusable algorithms. Using a greedy search over this grammar, we automatically choose the decomposition structure from raw data by evaluating only a small fraction of all models. …
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Scalable Approximate Inference and Model Selection in Gaussian Process Regression
Models with Gaussian process priors and Gaussian likelihoods are one of only a handful of Bayesian models where inference can be performed without the need for approximation. However, a frequent criticism of these models from practitioners of Bayesian machine learning is that they are challenging …
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Towards Improved Variational Inference for Deep Bayesian Models
… well-known that deep models trained via maximum likelihood estimation tend to be overconfident and give poorly-calibrated predictions. Bayesian deep learning attempts to address this by placing priors on the model parameters, which are then combined with a likelihood to perform posterior …
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Statistical Inference and Learning for Stochastic and Partial Differential Equations
… based estimation of SDE coefficients, with marginal-likelihood optimisation for hyperparameter tuning, we derive a likelihood-ratio test for detecting anomalies. Our proposed algorithm, SDE-GPR, was shown to outperform baseline anomaly detection methods in discriminative power. The following …
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Semiparametric Bayesian Approach using Weighted Dirichlet Process Mixture For Finance Statistical Models
… result compared to the canonical marginal likelihood approach. In the first topic, the response variable is modeled as the sum of three parts. One part is a linear function of covariates that enter the model parametrically. The second part is an additive nonparametric model. The …
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An Item Response Unfolding Model for Graphic Rating Scales
… The item parameters are estimated using maximum marginal likelihood estimation, and the standard errors of the estimates are computed from the observed information matrix. Simulation studies were conducted to investigate the behavior of the estimators in two simple versions of the model, one with …
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Multivariate Applications of Bayesian Model Averaging
… extension of univariate multiple regression. The marginal likelihood of a univariate multiple regression model has been approximated using the Bayes information criteria (BIC), hence the marginal likelihood for these multivariate extensions also makes use of this approximation. One of the main …
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Multivariate nonparametric estimation on censored panel data
… show that a nonparametric mass point approach to marginal likelihood estimation is possible due to Lindsay's characterization of the mixture density. However, their method makes the strong assumption that observed variables are uncorrelated with unobserved heterogeneity and that a small number of …
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Approximate Inference in Variational Autoencoders
… that they maximize a tighter lower bound on the marginal likelihood than the standard evidence lower bound. The first contribution of this thesis is to provide an alternative interpretation: that it optimizes the standard variational lower bound, but using a stochastic importance-weighted …
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Automatic model construction with Gaussian processes
… products of kernels, maximizing the approximate marginal likelihood. We show how any model in this class can be automatically decomposed into qualitatively different parts, and how each component can be visualized and described through text. We combine these results into a procedure that, given a …
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A study of Population MCMC for estimating Bayes Factors over nonlinear ODE models
… to sample from, often resulting in biased marginal likelihood estimates with large variances. Such problems are commonly encountered when modelling circardian rhythms, which exhibit highly nonlinear oscillatory dynamics and play a central role in the overall functioning of most organisms. …
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Dichotomous and polytomous item response model estimation using the marginal maximum likelihood and the EM algorithm
… are discussed in detail. The Bock and Lieberman Marginal Maximum T .ikelihood solution, the Bock and Aitkin Marginal Maximum Likelihood solution, and the Maximum Likelihood ability parameter estimation technique are also presented. fu addition to dichotomous models, several polytomous Item …
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Fast methods for fitting log-Gaussian Cox process models in ecology.
… quickly is challenging due to their intractable marginal likelihood which involves a high dimensional integral to account for the latent Gaussian field - leading to large spatial variance-covariance matrices. In this thesis we address these using a novel combination of variational approximation …
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Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning
… process priors and can be learned using the marginal likelihood. To make inference tractable, we develop a variational inference scheme that uses unbiased estimates of intractable covariance functions. We then address the mismatch between aggregate posteriors and priors in variational …
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Physics-Based Statistical Learning in Thermoacoustics
… with the MCMC, we quantitatively compare the marginal likelihood of the data for four tuned heat release rate models, thus finding the best performing model. Because it is physics-based, we find that the best model is quantitatively accurate, with known error bounds, significantly beyond the …
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Semiparametric Methods for the Generalized Linear Model
… in the SGLM-L is developed. We construct a log-likelihood ratio test for inference. In the second part of this dissertation, we use a single index model to generalize the GLMM to have a linear combination of covariates enter the model via a nonparametric mean function, because the linear model …
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Multiscale modelling of woven and knitted fabric membranes
… cross-validation technique by optimising the log marginal likelihood. In the macroscale, textiles are modelled as nonlinear orthotropic membranes for which the stresses and material constitutive relations are predicted by the trained GPR model. This coupling between GPR and membrane models is …
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Methods for studying the neural code in high dimensions
… to the generalized linear model (GLM) log-likelihood that I developed in collaboration with my thesis advisor. This approximation is designed to ease the computational burden of evaluating GLMs. I will show that our method reduces the computational cost of evaluating the GLM log-likelihood …
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High dimensional information processing
… sparsity pattern is identified by the maximum-likelihood estimator. We find that this probability depends (inversely) exponentially on the difference of kXβk2 and the ℓ2-norm of Xβ projected onto the range of columns of X indexed by the wrong sparsity pattern. Second, when X is randomly drawn …
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