This paper investigates the optimization of Bayesian statistical models using deep learning techniques. We introduce the theoretical basis of Bayesian models and deep learning, and propose a novel ...

Applied Bayesian Modeling in Python Bayesian statistical methods offer a flexible and powerful framework for approaching a variety of data science problems. They provide results that are interpretable ...

Bayesian Data Analysis Python Demos This repository contains some Python demos for the book Bayesian Data Analysis, 3rd ed by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin (BDA3).

Obtaining high-quality solutions for constrained multi-objective optimization problems (CMOPs) has been extensively researched in recent years. One popular approach is the coevolutionary framework, ...

In this paper we study optimization problems with variational inequality constraints in finite dimensional spaces. Kuhn-Tucker type necessary optimality conditions involving coderivatives are given ...

If the space is discontinuous or highly constrained, it may shift toward evolutionary or surrogate-based methods. This adaptability is critical in PCB workflows, where the mix of continuous and ...

SEEQC folds clocking, pulse generation, feedback, and routing into a chip-level platform that sits inside the cryostat.