Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and ...

Probabilistic graphical models are a powerful technique for handling uncertainty in machine learning. The course will cover how probability distributions can be represented in graphical models, how ...

Bayesian statistics in contemporary data science signify a paradigm revolution in probabilistic argumentation that provides ...

Concerns over the impact of tariffs have prompted investors and consumers to lower their expectations for the U.S. economy.

Researchers develop a probabilistic framework using percolation theory to assess cascading cyber risks in drone swarms, enabling better insurance pricing and risk management for emerging drone ...