http://hdl.handle.net/11422/52 Sun, 19 Jul 2026 03:35:15 GMT 2026-07-19T03:35:15Z http://hdl.handle.net/11422/29680 Title: Bayesian quantile regression analysis of complex survey data under informative sampling Author(s)/Inventor(s): Nascimento, Marcus Gerardus Lavagnole Advisor: Gonçalves, Kelly Cristina Mota Abstract: The interest in considering the relation among random variables in quantiles instead of the mean has emerged in various fields. Data collected from complex survey designs are of fundamental importance to different areas. Combining both frameworks provides a powerful tool for supporting decisions and is useful for practitioners from diverse backgrounds. In this thesis, we aim to advance in this literature by investigating new developments and extensions of Bayesian methods for quantile regression analysis of complex survey data under informative sampling. We not only focus on the absolutely continuous case as previous works on the topic but also develop methods for count data and multiple outputs. Our methods are particularly appealing as they provide effective and easy-to-implement methodological tools. Publisher: Universidade Federal do Rio de Janeiro Type: Tese Mon, 01 Jan 2024 00:00:00 GMT http://hdl.handle.net/11422/29680 2024-01-01T00:00:00Z http://hdl.handle.net/11422/29634 Title: Alocação latente de Dirichlet para modelagem de tópicos em dissertações de mestrado em estatística e áreas correlatas no Brasil Author(s)/Inventor(s): Osorio, Juan Pablo Argote Advisor: Zanini, Carlos Tadeu Pagani Abstract: This master’s thesis addresses the topic modeling of master’s theses in statistics and related areas in Brazil, through Latent Dirichlet Allocation models. The main objective of the work is to infer the latent topics covered in these theses. First, the construction of a corpus of documents is discussed and presented, composed of the most recent theses from different Higher Education Institutions in Brazil, manually extracted from the web pages of each of the analyzed master’s programs. The inferential procedure adopted for the Latent Dirichlet Allocation model consists of Markov chain Monte Carlo methods and variational inference. Different methods for choosing the number of topics are also discussed, including information criteria such as Akaike, Bayesian, Deviance, Watanabe Akaike, and metrics based on the coherence of the inferred latent topics. The adopted methodology provides an in-depth understanding of the predominant topics in this corpus. Publisher: Universidade Federal do Rio de Janeiro Type: Dissertação Mon, 24 Feb 2025 00:00:00 GMT http://hdl.handle.net/11422/29634 2025-02-24T00:00:00Z http://hdl.handle.net/11422/27330 Title: Machine learning methods in music emotion recognition Author(s)/Inventor(s): Dessabato, Karolayne Pereira Advisor: Carvalho, Hugo Tremonte de Abstract: Music Emotion Recognition (MER), an area within Musical Information Retrieval (MIR), studies the emotions evoked in listeners by music. We address MER as a regression task, with the objective of predicting the emotional content of music (encoded in arousal and valence) from acoustic features extracted from the waveform. We apply an interpretable machine learning technique, investigating the role of these features in predicting the target variables. Initially, a random forest model is trained on the DEAM dataset (MediaEval Database for Emotional Analysis of Music). Then, we use the concept of Shapley values to interpret the role of each variable in the predictions made by this model. Finally, we extract the most significant features from the DEAM dataset to predict arousal and valence, thus enhancing the interpretability of the model employed. Additionally, we explore a dynamic linear model approach to gain further insights into the relationships between features and response variables. This method allows for a potentially “less black-box” and more interpretable representation of the problem. Principal Component Analysis (PCA) is also utilized to analyze the structure of features in the dataset, providing a more comprehensive understanding of the key variables influencing MER predictions. By integrating these approaches, we aim to enhance both the predictive performance and interpretability of the models, offering meaningful insights into the most relevant features that drive emotional responses in music. Publisher: Universidade Federal do Rio de Janeiro Type: Dissertação Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/11422/27330 2025-01-01T00:00:00Z http://hdl.handle.net/11422/27329 Title: Algoritmos para multi-armed bandits: teoria e aplicação à precificação dinâmica Author(s)/Inventor(s): Bastos, Ismael Sampaio Advisor: Iacobelli, Giulio Abstract: This work addresses the problem of sequential decision-making, focusing specifically on the multiarmed bandit (MAB) framework. In its classical formulation, the MAB problem involves an agent facing a row of slot machines (bandits), with a limited number of pulls (arms) available. The agent’s goal is to determine a sequence of actions that maximizes the total reward. The core challenge lies in balancing the trade-off between choosing the action that currently appears to yield the highest reward and exploring lesser-known alternatives (a dilemma known as exploration versus exploitation). In this study, we explore several algorithms designed to support decision-making within the multiarmed bandit setting. We also examine an application of this theory to the problem of dynamic pricing, i.e., determining optimal selling prices for products and services. In this context, the seller takes the role of the agent who aims to sell a product by selecting from a finite set of possible prices, without prior knowledge of demand or consumer behavior. The seller must therefore adopt a strategy that enables the identification of the optimal price over time. Publisher: Universidade Federal do Rio de Janeiro Type: Dissertação Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/11422/27329 2025-01-01T00:00:00Z