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.
Results
Showing 1 to 16 of 16 for “"Variational Bayesian"”.
-
Disentangling time constant and time dependent hidden state in time series with variational Bayesian inference
… and explore a new model architecture called a Variational Bayes Recurrent Neural Network (VBRNN) for modelling time series. The VBRNN contains explicit structure to disentangle time constant and time dependent dynamics for use with compatible time series, such as those that can be modelled by …
-
Multi-Way Block Models
… Block Models generalize model implementations to variational Bayesian, collapsed Gibbs sampling, collapsed variational Bayesian, and expectation propagation approaches. Comparative simulation studies show that the four implementation algorithms achieve meaningful parameter estimates for the latent …
-
Bayesian population inference for effective connectivity
… connectivity parameters is estimated in a Variational Bayesian (VB) framework, and structural model parameters are chosen by the corresponding evidence criterion. The significance of resulting connectivity statistics are evaluated by permutation-based approximations to the null …
-
A topic model based approach to inferring episodic directional selection in protein coding sequences
… A notable example of such techniques are the variational Bayesian methods. We show that our approach performs well in terms of specificity and power, and demonstrate its utility by applying it to some real datasets of HIV sequences.
-
Weighting protein ensembles with Bayesian statistics and small-angle X-ray scattering data
… a given set of experimental observables. The Variational Bayesian Weighting program uses Bayesian statistics to fit conformational ensembles, and in doing so also quantifies the uncertainty in the underlying ensemble. The present work sought to introduce new functionality to this program, …
-
A Dual Metamodeling Perspective for Design and Analysis of Stochastic Simulation Experiments
… of the computational budget. Third, we propose a variational Bayesian inference-based Gaussian process (VBGP) metamodeling approach to accommodate the situation where either one or multiple simulation replications are available at every design point. VBGP can fit the mean and variance response …
-
GAUSSIAN PROCESSES FOR 3D SHAPE MODELLING OF NOISY AND INCOMPLETE DATA. AN APPLICATION TO HUMAN EARS RECONSTRUCTION
… a method for parameter estimation based on Variational Bayesian Inference. Not only do we achieve better registration results in the presence of large regions of missing data, but we provide a more unified way to deal with shape modelling. This is a step towards a more coherent approach and …
-
The role of the BHLH038 transcription factor in the regulation of osmotic and drought stress responses in Arabidopsis thaliana
… work has revealed Gene Regulatory Networks using Variational Bayesian State Space Modelling, obtained from time-series slow drying microarray data. These Gene Regulatory Networks unveiled various Transcription Factors such as BHLH038 closely related to AGL22 a key hub gene for drought response in …
-
Computation tools for the Fourier transform infrared (FT-IR) spectroscopic imaging
… the noiseless absorbance data. Then, novel variational Bayesian deconvolution algorithms using a theoretical formula of the optical point spread function (PSF) are used to improve the spatial resolution, and estimate the mismatching term in the true and theoretical PSF. For sparse …
-
Order, disorder, and protein aggregation
… of Parkinson's disease, constructed using a Variational Bayesian Weighting algorithm in combination with NMR data collected by our collaborators. We find that the data fit a description in which the protein predominantly exists as a disordered monomer but contains small quantities of …
-
Statistical Inference and Learning for Stochastic and Partial Differential Equations
… distribution over the PDE solution. Taking a Bayesian approach in both cases, we aim to quantify the uncertainty on the solution via the posterior distribution, whilst estimating unknown physical model parameters. Our second contribution considers the case of a known observation model. We …
-
Fast algorithms for Bayesian variable selection
… on penalized likelihood, and the other based on Bayesian framework. We focus on the Bayesian framework in which a hierarchical prior is imposed on all unknown parameters including the unknown variable set. The Bayesian approach has many advantages, for example, we can access unknown obtain the …
-
Artificial General Intelligence (AGI)-Native Wireless Systems: Digital Twins and World Models for Beyond 6G Networks
… for this POMDP, this problem is solved through a variational Bayesian approach that unifies perception, planning, action, and learning under the minimization of variational and expected free energy. Results showcase how the proposed framework yields AI agents that can act, reason, learn, and …
-
Image classification and feature selection
Made available in DSpace on 2012-06-27T21:22:52Z (GMT). No. of bitstreams: 9 Chen_Gang.pdf: 2139361 bytes, checksum: 3e14f03bb001785a473bc6193dc54f8b (MD5) license.txt: 4058 bytes, checksum: 50cddeb1b191bb1536258b22551f35da (MD5) ch4.tex: 87063 bytes, checksum: 8fac0f031e2c4b465952bb040f6131fa …
-
Bayesovské přístupy pro adaptivní identifikaci systémů
Práce se zabývá bayesovskou identifikací časově variantních normálních regresních modelů a skýtá celkem čtyři hlavní algoritmy. První dvě uvedené algoritmizace slouží pro průběžnou regularizovanou identifikaci jednoho regresního modelu. Neznámost časového vývoje tohoto modelu je u obou …