# A melhor ferramenta para a sua pesquisa, trabalho e TCC!

## Bayesian learning of visual chunks by human observers

## Sparse Bayesian Learning for Identifying Imaging Biomarkers in AD Prediction

## Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory

## Bayesian learning and the psychology of rule induction

## An evaluation of factors influencing Bayesian learning systems.

## Non-parametric Bayesian Learning with Deep Learning Structure and Its Applications in Wireless Networks

## Prior Support Knowledge-Aided Sparse Bayesian Learning with Partly Erroneous Support Information

## PAC-Bayesian Learning and Domain Adaptation

## A scaled gradient projection method for Bayesian learning in dynamical systems

## Efficient Bayesian Learning in Social Networks with Gaussian Estimators

## Bayesian learning of noisy Markov decision processes

## A Robust Independence Test for Constraint-Based Learning of Causal Structure

## Bayesian Learning of Neural Networks for Signal/Background Discrimination in Particle Physics

## Computationally Efficient Sparse Bayesian Learning via Generalized Approximate Message Passing

## On The Sparse Bayesian Learning Of Linear Models

## Bayesian Learning of Loglinear Models for Neural Connectivity

## Hidden states, hidden structures: Bayesian learning in time series models

## Bayesian Learning Using Automatic Relevance Determination Prior with an Application to Earthquake Early Warning

## Non-parametric Bayesian Learning with Incomplete Data

In most machine learning approaches, it is usually assumed that data are complete. When data are partially missing due to various reasons, for example, the failure of a subset of sensors, image corruption or inadequate medical measurements, many learning methods designed for complete data cannot be directly applied. In this dissertation we treat two kinds of problems with incomplete data using non-parametric Bayesian approaches: classification with incomplete features and analysis of low-rank matrices with missing entries.

Incomplete data in classification problems are handled by assuming input features to be generated from a mixture-of-experts model, with each individual expert (classifier) defined by a local Gaussian in feature space. With a linear classifier associated with each Gaussian component, nonlinear classification boundaries are achievable without the introduction of kernels. Within the proposed model, the number of components is theoretically ``infinite'' as defined by a Dirichlet process construction, with the actual number of mixture components (experts) needed inferred based upon the data under test. With a higher-level DP we further extend the classifier for analysis of multiple related tasks (multi-task learning)...

## GPU-Based Sparse Bayesian Learning for Adaptive Transmission Tomography

The aim of this thesis is to propose and investigate a GPU-based scalable image reconstruction algorithm for transmission tomography based on a Gaussian noise model for the log transformed and calibrated measurements. The proposed algorithm is based on sparse Bayesian learning (SBL) which promotes sparsity of the imaged object by introducing additional latent variables, one for each pixel/voxel, and learning them from the data using an hierarchical Bayesian model.

We address the computational bottleneck of SBL which arises in the computation of posterior variances. Two scalable methods for efficient estimation of variances were studied and tested: the first is based on a matrix probing technique; and the second method is based on a Monte Carlo estimator. Finally, we propose an experimental CT system where instead of using a standard scan around the object, the source locations are selected based on the learned information from previously available measurements, leading to fewer projections.

The keys advantages of the proposed algorithm are: (1) It uses smooth penalties, thus allowing the use of standard gradient-based methods; (2) It does not require any tuning of nuisance parameters; (3) It is highly parallelizable and scalable; (4) It enables adaptive sensing where the measurements are chosen sequentially based on the mutual information measure.

; Thesis