This is a preview. Log in through your library . Abstract This paper introduces a Bayesian decision theoretic model of optimal production in the presence of learning-curve uncertainty. The well-known ...

Bayesian Learning is becoming more feasible and attracting greater interest in mining. But adopting it also comes with some challenges. For one thing, this is a highly specialised branch of statistics ...

Machine Learning gets all the marketing hype, but are we overlooking Bayesian Networks? Here's a deeper look at why "Bayes Nets" are underrated - especially when it comes to addressing probability and ...

MIT’s scientists claim they can teach a new concept to a computer using a single example rather than thousands. if confirmed, this significantly reduced the requirements needed for machine learning.

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 ...

Bayesian learning claims that the strength of the price impact of unanticipated information depends on the relative precision of traders' prior and posterior beliefs. In this paper, we test for this ...

The goal of the consortium is to develop innovative quantitative methods to improve the characterization of subsurface reservoirs for hydrocarbon exploration and carbon dioxide sequestration and ...

Prerequisites: COMP_SCI grad standing OR (COMP_SCI 214 and (MATH 240-0 or GEN_ENG 205-1 or GEN_ENG 206-1) and (IEMS 201-0 or IEMS 303-0 or ELEC_ENG 302-0 or STAT 210-0 or MATH 310-1). Stat 304 is *not ...