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

## Redes probabilísticas: aprendendo estruturas e atualizando probabilidades; Probabilistic networks: learning structures and updating probabilities

## Using Mechanistic Bayesian Networks to Identify Downstream Targets of the Sonic Hedgehog Pathway

## Input Warping for Bayesian Optimization of Non-Stationary Functions

## A scoring function for learning Bayesian networks based on mutual information and conditional independence tests

## Using bayesian networks and parameterized questions in independent study

## Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-Parameters

## Dynamic Bayesian Multinets

## BayesPy: Variational Bayesian Inference in Python

## Update Rules for Parameter Estimation in Bayesian Networks

## Bayesian Network Structure Learning Using Quantum Annealing

## Bayesian test of significance for conditional independence: The multinomial model

## A Bayesian encourages dropout

## Learning networks determined by the ratio of prior and data

## Improving parameter learning of Bayesian nets from incomplete data

## Robust Bayesian reinforcement learning through tight lower bounds

## Estimating Continuous Distributions in Bayesian Classifiers

## PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers

## Discriminative Bayesian Dictionary Learning for Classification

## ABC Reinforcement Learning

## Nonparametric Bayesian Context Learning for Buried Threat Detection

This dissertation addresses the problem of detecting buried explosive threats (i.e., landmines and improvised explosive devices) with ground-penetrating radar (GPR) and hyperspectral imaging (HSI) across widely-varying environmental conditions. Automated detection of buried objects with GPR and HSI is particularly difficult due to the sensitivity of sensor phenomenology to variations in local environmental conditions. Past approahces have attempted to mitigate the effects of ambient factors by designing statistical detection and classification algorithms to be invariant to such conditions. These methods have generally taken the approach of extracting features that exploit the physics of a particular sensor to provide a low-dimensional representation of the raw data for characterizing targets from non-targets. A statistical classification rule is then usually applied to the features. However, it may be difficult for feature extraction techniques to adapt to the highly nonlinear effects of near-surface environmental conditions on sensor phenomenology, as well as to re-train the classifier for use under new conditions. Furthermore, the search for an invariant set of features ignores that possibility that one approach may yield best performance under one set of terrain conditions (e.g....