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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 389 for “"Bayesian Inference"”.
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Bayesian Inference in Regression
Made available in DSpace on 2014-12-13T18:21:45Z (GMT). No. of bitstreams: 1 7709067.pdf: 7574042 bytes, checksum: 92b2c4bc545c6d4ae67a5fa75e6ee06f (MD5) Previous issue date: 1976
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Robustness in Bayesian Inference
Made available in DSpace on 2014-12-13T18:22:18Z (GMT). No. of bitstreams: 1 8009105.pdf: 3493399 bytes, checksum: 88facf4ec77a9a0c95fec3b9fb79bb15 (MD5) Previous issue date: 1979
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Bayesian Inference of Phylogenetic Networks
… in the development of methods for species tree inference under the MSC, owing mainly to the accumulating evidence of incomplete lineage sorting in phylogenomic analyses. However, the evolutionary history of a set of genomes, or species, could be reticulate due to the occurrence of evolutionary …
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Bayesian inference algorithm on Raw
… hardware platform developed at MIT, running a Bayesian inference algorithm. Motivation for examining this parallel system is a growing interest in creating a self-learning and cognitive processor, which these hardware and software components can potentially produce. The Bayesian inference …
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Using Bayesian Inference in Design Applications
… The tools and methods previously developed for Bayesian inference are adapted and utilized to solve design problems. Given a desired design output, Bayesian parameter estimation and model comparison are employed to produce designs that meet the prescribed design specifications and requirements. …
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Bayesian Inference in Nonparametric Logistic Regression
… as a function of the predictor variable, and in inferences about the estimated function. The log-odds (logit) of the probability is estimated nonparametrically, using generalized smoothing splines.
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Approximate Bayesian inference and optimal transport
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms
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Adjoint-accelerated Bayesian inference in thermoacoustics
… modelling, in which we use an efficient Bayesian inference framework to assimilate experimental data into thermoacoustic models. The framework provides four main tools: (i) parameter inference, (ii) uncertainty quantification, (iii) model comparison, and (iv) optimal experiment design. …
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Bayesian inference of stochastic dynamical models
A new methodology for Bayesian inference of stochastic dynamical models is developed. The methodology leverages the dynamically orthogonal (DO) evolution equations for reduced-dimension uncertainty evolution and the Gaussian mixture model DO filtering algorithm for nonlinear reduced-dimension state …
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Bayesian inference of chemical reaction networks
… but to also learn the model structure. Bayesian inference provides a natural approach for this data-driven construction of models. Traditional Bayesian model inference methodology is based on evaluating a multidimensional integral for each model. This approach is often infeasible for …
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Latent variable augmentation for approximate Bayesian inference
Performing inference on probabilistic models can represent a challenge even in seemingly simple problems. When working with non-conjugate Bayesian models, we need approximate methods such as variational inference or sampling, each with its pitfalls and limits. For instance, heavy-tailed …
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Contributions to Bayesian inference via spectral methods
This thesis investigates Bayesian inference methods for time series and spatial models in the frequency domain. One of the main drawbacks of Bayesian inference in this setting is the computational burden, especially for large data. Using ideas from Fourier analysis, the original signal (data) …
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Computational Bayesian inference using low discrepancy sequences
… 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 integration scheme for which …
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Bayesian Inference of the Weibull-Pareto Distribution
… and actuarial data. In this work a hierarchical Bayesian model was developed using the Weibull-Pareto distribution.</p>
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A new evidence measure for Bayesian Inference
… to build a measure of evidence that covers, in a Bayesian context, the role that the p-value has played in the frequentist setting. A prominent example is the decision test based on the Bayes Factor. Worth to mention it is also the e-value, another Bayesian evidence measure on which the Full …
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Weighting and moment conditions in Bayesian inference
… thesis was motivated by the goal of developing Bayesian methods for "weighted" biomedical data. To be more specific, we are referring to probability weights, which are used to adjust for distributional differences between the sample and the population. Sometimes, these differences occur by …
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Iterative Monte Carlo Approximations for Bayesian Inference
The common theme of this thesis is the concept of using Monte Carlo techniques to approximate a sequence of probability distributions. Novel methodological contributions are found in Chapter 3 through to Chapter 6. In Chapter 3 we derive a method for the complete characterisation of online …
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An optimization based algorithm for Bayesian inference
In the Bayesian statistical paradigm, uncertainty in the parameters of a physical 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 …
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Some advances in Bayesian inference and generative modeling
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms
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