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

## Bayesian network classifiers: Beyond classification accuracy

## Action selection performance of a reconfigurable basal ganglia inspired model with Hebbian–Bayesian Go-NoGo connectivity

## Local Nonstationarity for Efficient Bayesian Optimization

## Bayesian and L1 Approaches to Sparse Unsupervised Learning

## CTBNCToolkit: Continuous Time Bayesian Network Classifier Toolkit

## Incorporating Type II Error Probabilities from Independence Tests into Score-Based Learning of Bayesian Network Structure

## Differentially Private Bayesian Optimization

## Risk and Regret of Hierarchical Bayesian Learners

## No-Regret Learning in Bayesian Games

## Maximum Margin Bayesian Networks

## The Bayesian Structural EM Algorithm

## Learning to Select Pre-Trained Deep Representations with Bayesian Evidence Framework

## Learning Discrete Bayesian Networks from Continuous Data

## SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure

## Unsupervised Learning of Noisy-Or Bayesian Networks

## Learning Equivalence Classes of Bayesian Networks Structures

## Generalised Bayesian matrix factorisation models

## Nonparametric Bayesian Models for Joint Analysis of Imagery and Text

It has been increasingly important to develop statistical models to manage large-scale high-dimensional image data. This thesis presents novel hierarchical nonparametric Bayesian models for joint analysis of imagery and text. This thesis consists two main parts.

The first part is based on single image processing. We first present a spatially dependent model for simultaneous image segmentation and interpretation. Given a corrupted image, by imposing spatial inter-relationships within imagery, the model not only improves reconstruction performance but also yields smooth segmentation. Then we develop online variational Bayesian algorithm for dictionary learning to process large-scale datasets, based on online stochastic optimization with a natu- ral gradient step. We show that dictionary is learned simultaneously with image reconstruction on large natural images containing tens of millions of pixels.

The second part applies dictionary learning for joint analysis of multiple image and text to infer relationship among images. We show that feature extraction and image organization with annotation (when available) can be integrated by unifying dictionary learning and hierarchical topic modeling. We present image organization in both "flat" and hierarchical constructions. Compared with traditional algorithms feature extraction is separated from model learning...