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 120 for “"Posterior distribution"”.
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Learning distributions with Particle Mirror Descent
… effective and provable primal method to estimate posterior distribution, Particle Mirror Descent is appealing for its simplicity and flexibility. In this thesis we explore the applications of Particle Mirror Descent in both supervised and unsupervised learning. In the general classification …
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Fast EM Based Posterior Approximation for IRT Item Parameters
… that has recently become popular for this is posterior predictive model checking (PPMC). This is commonly implemented using the joint posterior distribution of the item parameters as estimated through Markov chain Monte Carlo (MCMC). MCMC estimation is typically very computationally intensive …
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Bayesian Analysis of Linear Inverse Problems with Applications in Economics and Finance
… and the solution to the inference problem is the posterior distribution of this parameter. A regular version of the posterior distribution in functional spaces is characterized. However, the infinite dimension of the considered spaces causes a problem of non continuity of the solution and then a …
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Bayesian modelling of nuclear fusion experiments
… inference. The joint model provides the joint posterior probability distribution of the physics parameters, hyperparameters and other unknown parameters, such as calibration factors. This thesis theoretically and experimentally shows that the joint posterior distribution intrinsically embodies …
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Bayesian analysis of finite mixture distributions using the allocation sampler
Finite mixture distributions are receiving more and more attention from statisticians in many different fields of research because they are a very flexible class of models. They are typically used for density estimation or to model population heterogeneity. One can think of a finite mixture …
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Fast algorithms for Bayesian variable selection
… for example, we can access unknown obtain the posterior distribution of the sub-models. And more accurate prediction may be obtained by model averaging. However, as the posterior distribution of the model parameters is usually not in closed form, posterior inference that relies on Markov Chain …
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Some theoretical and applied results concerning item response theory model estimation
… Bayes unidimensional IRT modeling approach, the posterior distribution of examinee ability given test response is approximately normal for a long test. Under very general non-parametric assumptions, we make this claim rigorous for a broad class of latent models.
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Improved Sampling and Variational Inference Methods for Neural Networks
… (MCMC) constructed to directly sample from the posterior distribution over the network's weights, giving unbiased but high variance estimates. Second, approximate inference methods that construct or optimise an approximate posterior distribution, giving biased estimates, usually with low …
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Efficient MCMC inference for remote sensing of emission sources
… chain Monte Carlo sampling to jointly infer the posterior distribution of the number of emitters and emitter properties. Additionally, we develop performance metrics that can be efficiently computed using the sample-based representation of the posterior distribution. We investigate the expected …
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Linear Parameter Uncertainty Quantification using Surrogate Gaussian Processes
… a closed form expression of the resulting posterior distribution. We extend the method to weighted least squares and a Bayesian approach both with closed form expressions of the resulting posterior distributions. We test methods on 1D deconvolution and 2D tomography. Our new methods improve …
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An optimization based algorithm for Bayesian inference
… system is characterized by a probability distribution. Information from observations is incorporated by updating this distribution from prior to posterior. Quantities of interest, such as credible regions, event probabilities, and other expectations can then be obtained from the posterior …
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A Bayesian approach to feed reconstruction
… to sample from the resulting high-dimensional posterior distribution. We reviewed and implemented different algorithms to generate samples from this posterior that satisfy the given constraints. We tested our approach on a data set from a plant.
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Markov chain Monte Carlo methodoloy for inference with generalised linear spatial models
… which is usually characterised by a continuous distribution. Such models are widely used in geostatistics where a continuous spatial phenomenon is modelled through an underlying latent Gaussian process. If the observed data are also Gaussian then inference for the underlying process and the …
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Fast and efficient approaches to large-scale occupancy models
… two Variational Bayes approximations to the posterior distribution of the parameters of a single season site occupancy model that uses logistic or probit link functions to model the probability of species occurrence at sites and species detection probabilities. The results suggest that under …
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On the low-dimensional structure of Bayesian inference
… and predictions through the exploration of their posterior distributions, i.e., their distributions conditioned on available data. The Bayesian paradigm provides a flexible, principled framework for quantifying uncertainty, wherein heterogeneous and incomplete sources of information (e.g., prior …
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Bayesian estimation of stochastic volatility models with fat tails and correlated errors applied to the South African financial market
… sampler is used to sample parameters from the posterior distribution. Volatility is used as measure of an asset's risk. It is particularly important in risk management, derivatives pricing, and portfolio selection. When pricing derivatives it is important to quote the correct volatility trading …
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Fiducial probability theory
… is generalized to that of finding a fiducial distribution for the parameter. Necessary conditions for application of fiducial theory are considered. Examples are given to illustrate the methods used. Particular attention is given to the meaning which should be associated with a fiducial …
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A Bayesian Approach to Beamforming for Uncertain Direction -of -Arrival
… combined according to the data-driven posterior distribution. As the number of recevied data increases, the Bayesian beamformer asymptotically converges into a directional beamformer that points at the closest admissible direction to the true underlying direction, where closeness is …
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Probabilistic search: a Bayesian approach in a continuous workspace
… obtain the general filtering equations for the posterior distribution representing the object's location over the workspace. Given a likelihood and prior belief belonging to the exponential family class, while using this class's self-conjugacy property, an exact, finite representation of the …
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Some Model-Based and Distance-Based Clustering Methods for Characterization of Regional Ecological Stressor-Response Patterns and Regional Environmental Quality Trends
… We rely on a Bayesian formulation and sample the posterior distribution using a Markov chain Monte Carlo algorithm. Two general approaches to the label-switching problem are considered, each leading to procedures that we apply in data analyses. Two applications are presented. We explore some …
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