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

## Bayesian Nonparametric Kernel-Learning

## Bayesian Inference in Sparse Gaussian Graphical Models

## Monte Carlo Bayesian Reinforcement Learning

## Smoothness and Structure Learning by Proxy

## Bayesian Multicategory Support Vector Machines

## Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm

## Learning Bayesian Networks with Restricted Causal Interactions

## Advances in Learning Bayesian Networks of Bounded Treewidth

## Bayesian representation learning with oracle constraints

## Data mining for censored time-to-event data: A Bayesian network model for predicting cardiovascular risk from electronic health record data

## Improved learning of Bayesian networks

## Bayesian image segmentations by Potts prior and loopy belief propagation

## Tree Exploration for Bayesian RL Exploration

## Preference elicitation and inverse reinforcement learning

## Convex Point Estimation using Undirected Bayesian Transfer Hierarchies

## Freeze-Thaw Bayesian Optimization

## Online Learning of Non-Stationary Networks, with Application to Financial Data

In this paper, we propose a new learning algorithm for non-stationary Dynamic Bayesian Networks is proposed. Although a number of effective learning algorithms for non-stationary DBNs have previously been proposed and applied in Signal Pro- cessing and Computational Biology, those algorithms are based on batch learning algorithms that cannot be applied to online time-series data. Therefore, we propose a learning algorithm based on a Particle Filtering approach so that we can apply that algorithm to online time-series data. To evaluate our algorithm, we apply it to the simulated data set and the real-world financial data set. The result on the simulated data set shows that our algorithm performs accurately makes estimation and detects change. The result applying our algorithm to the real-world financial data set shows several features, which are suggested in previous research that also implies the effectiveness of our algorithm.

; Thesis## Efficient Bayesian Nonparametric Methods for Model-Free Reinforcement Learning in Centralized and Decentralized Sequential Environments

As a growing number of agents are deployed in complex environments for scientific research and human well-being, there are increasing demands for designing efficient learning algorithms for these agents to improve their control polices. Such policies must account for uncertainties, including those caused by environmental stochasticity, sensor noise and communication restrictions. These challenges exist in missions such as planetary navigation, forest firefighting, and underwater exploration. Ideally, good control policies should allow the agents to deal with all the situations in an environment and enable them to accomplish their mission within the budgeted time and resources. However, a correct model of the environment is not typically available in advance, requiring the policy to be learned from data. Model-free reinforcement learning (RL) is a promising candidate for agents to learn control policies while engaged in complex tasks, because it allows the control policies to be learned directly from a subset of experiences and with time efficiency. Moreover, to ensure persistent performance improvement for RL, it is important that the control policies be concisely represented based on existing knowledge, and have the flexibility to accommodate new experience. Bayesian nonparametric methods (BNPMs) both allow the complexity of models to be adaptive to data...

## Machine Learning with Dirichlet and Beta Process Priors: Theory and Applications

Bayesian nonparametric methods are useful for modeling data without having to define the complexity of the entire model

The flexibility of Bayesian nonparametric priors arises from the prior's definition over an infinite dimensional parameter space. Therefore, there are theoretically an

This dissertation is split between novel practical applications and novel theoretical results for these priors. For the Dirichlet process, we investigate stick-breaking representations for the finite Dirichlet process and their application to novel sampling techniques...