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

## Sistema evolutivo eficiente para aprendizagem estrutural de redes Bayesianas; Efficient evolutionary system for learning BN structures

## Precipitates segmentation from scanning electron microscope images through machine learning techniques

## Learning overhypotheses with hierarchical Bayesian models

## Indirect evidence and the poverty of the stimulus: the case of anaphoric one

## Indirect evidence and the poverty of the stimulus: The case of anaphoric one

## Language evolution is shaped by the structure of the world: an iterated learning analysis

## Linear-Nonlinear-Poisson Neuron Networks Perform Bayesian Inference On Boltzmann Machines

## A new parameter Learning Method for Bayesian Networks with Qualitative Influences

## Marginal Pseudo-Likelihood Learning of Markov Network structures

## Asymptotics of Discrete MDL for Online Prediction

## Learning Optimal Augmented Bayes Networks

## Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC

## Near-Optimal Target Learning With Stochastic Binary Signals

## Learning Bayesian Network Parameters with Prior Knowledge about Context-Specific Qualitative Influences

## Convex Structure Learning for Bayesian Networks: Polynomial Feature Selection and Approximate Ordering

## A Kernel Approach to Tractable Bayesian Nonparametrics

## PAC-Bayesian Inequalities for Martingales

## BPR: Bayesian Personalized Ranking from Implicit Feedback

## A Bayesian Sampling Approach to Exploration in Reinforcement Learning

## Bayesian Nonparametric Modeling of Latent Structures

Unprecedented amount of data has been collected in diverse fields such as social network, infectious disease and political science in this information explosive era. The high dimensional, complex and heterogeneous data imposes tremendous challenges on traditional statistical models. Bayesian nonparametric methods address these challenges by providing models that can fit the data with growing complexity. In this thesis, we design novel Bayesian nonparametric models on dataset from three different fields, hyperspectral images analysis, infectious disease and voting behaviors.

First, we consider analysis of noisy and incomplete hyperspectral imagery, with the objective of removing the noise and inferring the missing data. The noise statistics may be wavelength-dependent, and the fraction of data missing (at random) may be substantial, including potentially entire bands, offering the potential to significantly reduce the quantity of data that need be measured. We achieve this objective by employing Bayesian dictionary learning model, considering two distinct means of imposing sparse dictionary usage and drawing the dictionary elements from a Gaussian process prior, imposing structure on the wavelength dependence of the dictionary elements.

Second...