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

## Physarum Learner: A bio-inspired way of learning structure from data

## Gene regulatory netwok reconstrution by bayesian integration of prior knowledge and/ordifferent experimental conditions

## Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

## PAC-Bayesian Analysis of Martingales and Multiarmed Bandits

## Bayesian Efficient Multiple Kernel Learning

## A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

## Bayesian Model Averaging Using the k-best Bayesian Network Structures

## Approximate Learning in Complex Dynamic Bayesian Networks

## Heteroscedastic Treed Bayesian Optimisation

## Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

## Near-Optimal Bayesian Active Learning with Noisy Observations

## Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo

## Learning Topic Models and Latent Bayesian Networks Under Expansion Constraints

## Practical Bayesian Optimization of Machine Learning Algorithms

## Stochastic complexity of Bayesian networks

## Unification of field theory and maximum entropy methods for learning probability densities

## Exploiting correlation and budget constraints in Bayesian multi-armed bandit optimization

## On the Sample Complexity of Learning Bayesian Networks

## Bayesian Multiregression Dynamic Models with Applications in Finance and Business

This thesis discusses novel developments in Bayesian analytics for high-dimensional multivariate time series. The focus is on the class of multiregression dynamic models (MDMs), which can be decomposed into sets of univariate models processed in parallel yet coupled for forecasting and decision making. Parallel processing greatly speeds up the computations and vastly expands the range of time series to which the analysis can be applied.

I begin by defining a new sparse representation of the dependence between the components of a multivariate time series. Using this representation, innovations involve sparse dynamic dependence networks, idiosyncrasies in time-varying auto-regressive lag structures, and flexibility of discounting methods for stochastic volatilities.

For exploration of the model space, I define a variant of the Shotgun Stochastic Search (SSS) algorithm. Under the parallelizable framework, this new SSS algorithm allows the stochastic search to move in each dimension simultaneously at each iteration, and thus it moves much faster to high probability regions of model space than does traditional SSS.

For the assessment of model uncertainty in MDMs, I propose an innovative method that converts model uncertainties from the multivariate context to the univariate context using Bayesian Model Averaging and power discounting techniques. I show that this approach can succeed in effectively capturing time-varying model uncertainties on various model parameters...

## Nonparametric Bayesian Dictionary Learning and Count and Mixture Modeling

Analyzing the ever-increasing data of unprecedented scale, dimensionality, diversity, and complexity poses considerable challenges to conventional approaches of statistical modeling. Bayesian nonparametrics constitute a promising research direction, in that such techniques can fit the data with a model that can grow with complexity to match the data. In this dissertation we consider nonparametric Bayesian modeling with completely random measures, a family of pure-jump stochastic processes with nonnegative increments. In particular, we study dictionary learning for sparse image representation using the beta process and the dependent hierarchical beta process, and we present the negative binomial process, a novel nonparametric Bayesian prior that unites the seemingly disjoint problems of count and mixture modeling. We show a wide variety of successful applications of our nonparametric Bayesian latent variable models to real problems in science and engineering, including count modeling, text analysis, image processing, compressive sensing, and computer vision.

; Dissertation