The key idea behind the probabilistic framework to machine learning is that learning can be thought of as inferring plausible models to explain observed data. A machine can use such models to make ...

eSpeaks’ Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...

Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

Bayesian optimization (BO) can help determine the optimal deposition conditions for high-performance passivation films in solar cells. However, simple implementations tend to suggest excessively thick ...

The Optimization, Data, and Decision Science (ODDS) Lab, established in late 2023 under the leadership of Dr. Ilgin Acar, represents a dynamic hub for cutting-edge research at the intersection of ...

This course offers a rigorous yet practical exploration of Bayesian reasoning for data-driven inference and decision-making. Students will gain a deep understanding of probabilistic modeling, and ...