Imagine a world where computational simulations can be inverted as easily as running them
forwards, where data can be used to refine models automatically, and where the only …

This article investigates maximum a-posteriori (MAP) estimation of autoregressive model
parameters when the innovations (errors) follow a finite mixture of distributions that, in turn …

Universal probabilistic programming systems (PPSs) provide a powerful framework for
specifying rich probabilistic models. They further attempt to automate the process of drawing …

This paper is concerned with the estimation problem of a periodic autoregressive model with
closed skew-normal innovations. The closed skew-normal (CSN) distribution has some …

Search in general, and heuristic search in particular, is at the heart of many Artificial
Intelligence algorithms and applications. There is now a growing and active community …

This thesis describes work on two applications of probabilistic programming: the learning of
probabilistic program code given specifications, in particular program code of one …

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 …

A number of intriguing decision scenarios revolve around partitioning a collection of objects
to optimize some application specific objective function. This problem is generally referred to …

The multi-armed bandit problem forms the foundation for solving a wide range of online
stochastic optimization problems through a simple, yet effective mechanism. One simply …

The multi-armed bandit problem has been studied for decades. In brief, a gambler
repeatedly pulls one out of N slot machine arms, randomly receiving a reward or a penalty …