Online optimization has gained increasing interest due to its capability of tracking real-world
streaming data. Although online optimization methods have been widely studied in the …

We consider the problem of performing Bayesian inference in probabilistic models where
observations are accompanied by uncertainty, referred to as" uncertain evidence.” We …

One of the most compelling features of probabilistic programming languages (PPLs) is their
ability to manipulate probability distributions as first-class objects. First-class representations …

It is well known that reinforcement learning can be cast as inference in an appropriate
probabilistic model. However, this commonly involves introducing a distribution over agent …

Graphical models in probability and statistics are a core concept in the area of probabilistic
reasoning and probabilistic programming-graphical models include Bayesian networks and …

Graphical models in probability and statistics are a core concept in the area of probabilistic
reasoning and probabilistic programming—graphical models include Bayesian networks …