We propose to represent a probability distribution as a program in a general-purpose programming language rather than a special language built from scratch. This approach makes it easier for the probabilistic-reasoning and programming-language communities to share their work. To demonstrate that this representation is simple and efficient, we implement inference by variable elimination and impo...

In recent years, declarative programming languages specialized for probabilistic modeling has emerged as distinct class of languages. These languages are predominantly written by researchers in the machine learning field and concentrate on generalized MCMC inference algorithm. Unfortunately, all these languages are too slow for practical adoption. In my talk, I will outline several places where...

Spreadsheet workbook contents are simple programs. Because of this, probabilistic programming techniques can be used to perform Bayesian inversion of spreadsheet computations. What is more, existing execution engines in spreadsheet applications such as Microsoft Excel can be made to do this using only built-in functionality. We demonstrate this by developing a native Excel implementation of bot...

Probabilistic programming languages allow domain experts to specify generative models in a high-level language, and reason about those models using domainindependent algorithms. Given an input, a probabilistic program generates a distribution over outputs. In this work, we instead use probabilistic programming to explicitly reason about the distribution over programs, rather than outputs. We pr...

Parsing models have many applications in AI, ranging from natural language processing (NLP) and computational music analysis to logic programming and computational learning. Broadly conceived, a parsing model seeks to uncover the underlying structure of an input, that is, the various ways in which elements of the input combine to form phrases or constituents and how those phrases recursively co...

This paper shows how probabilistic reasoning can be applied to the predicative style of programming.

Formal modelling languages such as process algebras are widespread and effective tools in computational modelling. However, handling data and uncertainty in a statistically meaningful way is an open problem in formal modelling, severely hampering the usefulness of these elegant tools in many real world applications. Here we introduce ProPPA, a process algebra which incorporates uncertainty in t...

In recent years sports analytics has gotten more and more popular. We propose a model for Rugby data in particular to model the 2014 Six Nations tournament. We propose a Bayesian hierarchical model to estimate the characteristics that bring a team to lose or win a game, and predict the score of

We present a data-driven, probabilistic trajectory optimization framework for systems with unknown dynamics, called Probabilistic Differential Dynamic Programming (PDDP). PDDP takes into account uncertainty explicitly for dynamics models using Gaussian processes (GPs). Based on the second-order local approximation of the value function, PDDP performs Dynamic Programming around a nominal traject...

We analyze the probabilistic variation of the multicommodity discrete network design problem named the probabilistic network design problem in which the commodities are generated probabilistically and the objective is to calculate the expected value of all possible network design instances. We extend the a priori strategy which has been successfully applied to the probabilistic variations of th...