Publication Date

2024

Document Type

Dissertation/Thesis

First Advisor

Ryu, Duchwan

Degree Name

Ph.D. (Doctor of Philosophy)

Legacy Department

Department of Statistics and Actuarial Science

Abstract

We consider Bayesian analyses for time-varying data from the opinion dynamics in social networks and the processes of sensorimotor learning. Firstly, understanding the underlying opinions of social media users is a difficult process, as they must be understood filtered through the messages sent. To understand how to best estimate the latent opinions, we use a Bayesian approach to estimate various characteristics of the users and the network. We present a model for using Bayesian methods for this problem, along with a simulation study to demonstrate effectiveness. Secondly, incentivization with punishments or rewards may affect human skill learning. To investigate the effects of the incentivization on the learning rates, the state space models have been used. Under the Bayesian framework, we have utilized a state space model with dynamically weighted particle filer and a functional data analysis model with Bayesian cubic P-splines. We present the estimated learning rates and the effect of the incentivization on the learning rates from two approaches, as well as the comparisons of their results.

Extent

121 pages

Language

en

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

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