Bayesian Structural Equation Modeling (BSEM) has gained significant attention due to its ability to resolve common issues found in frequentist approaches, such as nonconvergence, Heywood cases, sample size limitations, and inadmissible solutions. Furthermore, BSEM can estimate complex models that classical maximum likelihood methods often struggle with. A crucial component of BSEM is the incorporation of prior knowledge via prior distributions, which provides a unique advantage over frequentist methods by allowing previously known information to be transparently included in model specifications. Proper prior elicitation is essential for translating domain expertise into probability distributions, thus improving the accuracy and reliability of the model. Despite this, the development and widespread adoption of robust prior elicitation techniques in BSEM remain limited.

This project aims to advance the field of BSEM through innovative computational methods and practical applications, with a focus on GPU processing and its potential to enhance the efficiency of Bayesian computations. By leveraging parallel computing capabilities provided by modern graphic processing units, the project seeks to significantly accelerate the computational processes involved in BSEM. Additionally, the project will explore the use of BSEM in developing psychometric instruments, employing techniques such as Markov Chain Monte Carlo (MCMC) using the No-U-Turn Sampler (NUTS) for latent model estimation. These innovations promise to offer deeper insights into measurement precision and enhance the validity of psychometric instruments across diverse populations.

The expected outcomes of this project include improved computational efficiency, optimized model estimations, and broader adoption of Bayesian methods in applied research settings. By addressing both theoretical and practical challenges, this project aims to contribute to the broader use of BSEM in psychometric analysis.

BSEM requires intensive computations, especially for high-dimensional models, limiting its practical use. This project aims to enhance efficiency using GPU parallel computing, making Bayesian methods more accessible. Proper prior elicitation is vital yet underdeveloped. This project seeks to use standardized frameworks to improve accuracy and reliability.

Traditional psychometric methods struggle with uncertainty and prior knowledge integration. This project will adapt BSEM for psychometric development using advanced sampling techniques like MCMC (i.e., NUTS). 

By developing advanced statistical modeling techniques, the project contributes to improved educational outcomes and the advancement of knowledge in the fields of statistics and psychometrics (SDG 4. Quality Education).

The project promotes innovation in research methodologies, specifically in Bayesian Structural Equation Modeling, promoting technological advancement and enhancing research infrastructure (SDG 9. Industry, Innovation, and Infrastructure).

By collaborating with experts and utilizing high-performance computing resources, the project exemplifies the importance of partnerships in achieving sustainable development through knowledge sharing and cooperative research efforts (SDG 17. Partnerships for the Goals).

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With the objective to increase the research activity directed towards the achievement of the United Nations 2030 Sustainable Development Goals, the possibility of associating scientific projects with the Sustainable Development Goals is now available in Ciência_Iscte. These are the Sustainable Development Goals identified for this project. For more detailed information on the Sustainable Development Goals, click here.